Data Analyst training in Hyderabad helps you become an expert in Data Analytics, its concepts, terminology, and techniques. In this Data Analytics masters course, you will learn several analytics techniques, tools, programming languages like R and Python, working with SQL, implementing predictive analytics and statistics, and more. So, give a head start to your Data Analytics career by signing up for this Data Analyst certification in Hyderabad.
Online Classroom Training
Self Paced Training
In this Data Analytics training in Hyderabad, you will gain practical experience by working on industry-based projects that will help you understand how to solve real-time business problems. Also, in Data Analyst courses in Hyderabad, we will conduct many mock interview sessions, assist in creating and upgrading your resume, and more to give you confidence and prepare you for your job interviews.
Students, beginners, and professionals who wish to succeed in learning from our Data Analytics masters course must have some mathematical skills as a foundation.
The reasons why you should build your career in the Data Analytics field in Hyderabad are as follows:
So, enroll for the best Data Analytics Course in Hyderabad and land in your dream job as Data Analyst.
In this Data Analytics certification in Hyderabad, we aim to help you master the topics mentioned below:
Following are the individuals who must take up this Data Analytics course in Hyderabad and upskill themselves:
According to Glassdoor, the average income of a Data Analyst in the United States is about US$62,453 per annum. This may increase to US$95,000 per annum with more experience and better work quality.
In India, the average salary of these professionals is approximately ₹503,000 per annum and with more experience, it can rise to ₹1,005,000 per annum.
In this Data Analyst Course in Hyderabad, you will learn the following courses:
Online instructor-led courses:
On completing this Data Analytics Course in Hyderabad, Intellipaat will provide you with a Data Analyst certification after you complete the course. Moreover, you will receive certification from Microsoft and IBM which are among the top organizations in the world. These certificates aim to test your knowledge and skills in the field of Data Analyst.
Data Analyst is among the most sought-after career options in today’s technologically advanced world. There are numerous job opportunities available in this domain which is one of the main reasons why you can opt for this career option.
So, enroll in our Data Analytics courses in Hyderabad and land in your dream job as Data Analyst.
Here are a couple of differences between Data Scientists, Data Analysts, and Business Analysts:
Today Data Analytics is one of the top domains as we are living in a data-driven world. If you want to get ahead in your career, then you need to learn Data Analytics as it is being deployed in every organization regardless of the industry. Intellipaat Data Analyst courses in Hyderabad have been created to give you an edge in this data-driven world. Through this online training, you will work on real-world Data Analytics projects and case studies so that you can get a hands-on experience in this domain.
1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science, and its components
1.3 Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
1.4 Introduction to R programming and RStudio
1. Installation of RStudio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases
2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What are data exploratory analysis and data importing?
2.4 DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types
1. Accessing individual elements of customer churn data
2. Modifying and extracting results from the dataset using user-defined functions in R
3.1 Need for data manipulation
3.2 Introduction to the dplyr package
3.3 Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
3.4 Combining different functions with the pipe operator and implementing SQL-like operations with sqldf
1. Implementing dplyr
2. Performing various operations for manipulating data and storing it
4.1 Introduction to visualization
4.2 Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
4.5 Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
4.6 Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
4.7 Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
4.8 Geographic visualization with ggmap() and building web applications with shinyR
1. Creating data visualization to understand the customer churn ratio using ggplot2 charts
2. Using plotly for importing and analyzing data
3. Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot
5.1 Why do we need statistics?
5.2 Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
5.3 Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution
1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data
6.1 Introduction to Machine Learning
6.2 Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
6.3 Predicting results and finding the p-value and an introduction to logistic regression
6.4 Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
6.5 Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
6.6 Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables
1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable
7.1 Introduction to logistic regression
7.2 Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
7.3 Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
7.4 Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
7.5 Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
7.6 Real-life applications of logistic regression
1. Implementing predictive analytics by describing data
2. Explaining the relationship between one dependent binary variable and one or more binary variables
3. Using glm() to build a model, with ‘Churn’ as the dependent variable
8.1 What is classification? Different classification techniques
8.2 Introduction to decision trees
8.3 Algorithm for decision tree induction and building a decision tree in R
8.4 Confusion matrix and regression trees vs classification trees
8.5 Introduction to bagging
8.6 Random forest and implementing it in R
8.7 What is Naive Bayes? Computing probabilities
8.8 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
8.9 Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
1. Implementing random forest for both regression and classification problems
2. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
3. Using ROCR for performance metrics
9.1 What is Clustering? Its use cases
9.2 what is k-means clustering? What is canopy clustering?
9.3 What is hierarchical clustering?
9.4 Introduction to unsupervised learning
9.5 Feature extraction, clustering algorithms, and the k-means clustering algorithm
9.6 Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
9.7 Dendograms, understanding hierarchical clustering, and implementing it in R
9.8 Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data
10.1 Introduction to association rule mining and MBA
10.2 Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
10.3 Introduction to recommendation engines
10.4 User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
10.5 Recommendation engine use cases
1. Deploying association analysis as a rule-based Machine Learning method
2. Identifying strong rules discovered in databases with measures based on interesting discoveries
11.1 Introducing Artificial Intelligence and Deep Learning
11.2 What is an artificial neural network? TensorFlow: The computational framework for building AI models
11.3 Fundamentals of building ANN using TensorFlow and working with TensorFlow in R
12.1 What is a time series? The techniques, applications, and components of time series
12.2 Moving average, smoothing techniques, and exponential smoothing
12.3 Univariate time series models and multivariate time series analysis
12.4 ARIMA model
12.5 Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
1. Analyzing time series data
2. Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series
13.1 Introduction to Support Vector Machine (SVM)
13.2 Data classification using SVM
13.3 SVM algorithms using separable and inseparable cases
13.4 Linear SVM for identifying margin hyperplane
14.1 What is the Bayes theorem?
14.2 What is Naïve Bayes Classifier?
14.3 Classification Workflow
14.4 How Naive Bayes classifier works and classifier building in Scikit-Learn
14.5 Building a probabilistic classification model using Naïve Bayes and the zero probability problem
15.1 Introduction to the concepts of text mining
15.2 Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
15.3 Text mining algorithms and the quantification of the text
15.4 TF-IDF and after TF-IDF
Case Study 01: Market Basket Analysis (MBA)
1.1 This case study is associated with the modeling technique of Market Basket Analysis, where you will learn about loading data, plotting items, and running algorithms.
1.2 It includes finding out the items that go hand in hand and can be clubbed together.
1.3 This is used for various real-world scenarios like a supermarket shopping cart and so on.
Case Study 02: Logistic Regression
2.1 In this case study, you will get a detailed understanding of the advertisement spends of a company that will help drive more sales.
2.2 You will deploy logistic regression to forecast future trends.
2.3 You will detect patterns and uncover insight using the power of R programming.
2.4 Due to this, the future advertisement spends can be decided and optimized for higher revenues.
Case Study 03: Multiple Regression
3.1 You will understand how to compare the miles per gallon (MPG) of a car based on various parameters.
3.2 You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc.
3.3 The case study includes model building, model diagnostic, and checking the ROC curve, among other things.
Case Study 04: Receiver Operating Characteristic (ROC)
4.1 In this case study, you will work with various datasets in R.
4.2 You will deploy data exploration methodologies.
4.3 You will also build scalable models.
4.4 Besides, you will predict the outcome with highest precision, diagnose the model that you have created with real-world data, and check the ROC curve.
Market Basket Analysis
This is an inventory management project where you will find the trends in the data that will help the company to increase sales. In this project, you will be implementing association rule mining, data extraction, and data manipulation for the Market Basket Analysis.
Credit Card Fraud Detection
The project consists of data analysis for various parameters of banking dataset. You will be using a V7 predictor, V4 predictor for analysis, and data visualization for finding the probability of occurrence of fraudulent activities.
Loan Approval Prediction
In this project, you will use the banking dataset for data analysis, data cleaning, data preprocessing, and data visualization. You will implement algorithms such as Principal Component Analysis and Naive Bayes after data analysis to predict the approval rate of a loan using various parameters.
Netflix Recommendation System
Implement exploratory data analysis, data manipulation, and visualization to understand and find the trends in the Netflix dataset. You will use various Machine Learning algorithms such as association rule mining, classification algorithms, and many more to create movie recommendation systems for viewers using Netflix dataset.
Case Study 1: Introduction to R Programming
In this project, you need to work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for various organizational needs.
Case Study 2: Solving Customer Churn Using Data Exploration
Use data exploration in order to understand what needs to be done to make reductions in customer churn. In this project, you will be required to extract individual columns, use loops to work on repetitive operations, and create and implement filters for data manipulation.
Case Study 3: Creating Data Structures in R
Implement numerous data structures for numerous possible scenarios. This project requires you to create and use vectors. Further, you need to build and use metrics, utilize arrays for storing those metrics, and have knowledge of lists.
Case Study 4: Implementing SVD in R
Utilize the dataset of MovieLens to analyze and understand single value decomposition and its use in R programming. Further, in this project, you must build custom recommended movie sets for all users, develop a collaborative filtering model based on the users, and for a movie recommendation, you must create realRatingMatrix.
Case Study 5: Time Series Analysis
This project required you to perform TSA and understand ARIMA and its concepts with respect to a given scenario. Here, you will use the R programming language, ARIMA model, time series analysis, and data visualization. So, you must understand how to build an ARIMA model and fit it, find optimal parameters by plotting PACF charts, and perform various analyses to predict values.
1.1 What is data visualization?
1.2 Comparison and benefits against reading raw numbers
1.3 Real use cases from various business domains
1.4 Some quick and powerful examples using Tableau without going into the technical details of Tableau
1.5 Installing Tableau
1.6 Tableau interface
1.7 Connecting to DataSource
1.8 Tableau data types
1.9 Data preparation
2.1 Installation of Tableau Desktop
2.2 Architecture of Tableau
2.3 Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
2.4 How to start with Tableau
2.5 The ways to share and export the work done in Tableau
1. Play with Tableau desktop
2. Learn about the interface
3. Share and export existing works
3.1 Connection to Excel
3.2 Cubes and PDFs
3.3 Management of metadata and extracts
3.4 Data preparation
3.5 Joins (Left, Right, Inner, and Outer) and Union
3.6 Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.
1. Connect to Excel sheet to import data
2. Use metadata and extracts
3. Manage NULL values
4. Clean up data before using
5. Perform the join techniques
6. Execute data blending from multiple sources
4.1 Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies)
4.2 Constant sets
4.3 Computed sets, bins, etc.
1. Use marks to create and edit sets
2. Highlight the desired items
3. Make groups
4. Apply sorting on results
5. Make hierarchies among the created sets
5.1 Filters (addition and removal)
5.2 Filtering continuous dates, dimensions, and measures
5.3 Interactive filters, marks card, and hierarchies
5.4 How to create folders in Tableau
5.5 Sorting in Tableau
5.6 Types of sorting
5.7 Filtering in Tableau
5.8 Types of filters
5.9 Filtering the order of operations
1. Use the data set by date/dimensions/measures to add a filter
2. Use interactive filter to view the data
3. Customize/remove filters to view the result
6.1 Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
6.2 Formatting data using labels and tooltips
6.3 Edit axes and annotations
6.4 K-means cluster analysis
6.5 Trend and reference lines
6.6 Visual analytics in Tableau
6.7 Forecasting, confidence interval, reference lines, and bands
1. Apply labels and tooltips to graphs, annotations, edit axes’ attributes
2. Set the reference line
3. Perform k-means cluster analysis on the given dataset
7.1 Working on coordinate points
7.2 Plotting longitude and latitude
7.3 Editing unrecognized locations
7.4 Customizing geocoding, polygon maps, WMS: web mapping services
7.5 Working on the background image, including add image
7.6 Plotting points on images and generating coordinates from them
7.7 Map visualization, custom territories, map box, WMS map
7.8 How to create map projects in Tableau
7.9 Creating dual axes maps, and editing locations
1. Plot longitude and latitude on a geo map
2. Edit locations on the geo map
3. Custom geocoding
4. Use images of the map and plot points
5. Find coordinates
6. Create a polygon map
7. Use WMS
8.1 Calculation syntax and functions in Tableau
8.2 Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
8.3 LOD expressions, including concept and syntax
8.4 Aggregation and replication with LOD expressions
8.5 Nested LOD expressions
8.6 Levels of details: fixed level, lower level, and higher level
8.7 Quick table calculations
8.8 The creation of calculated fields
8.9 Predefined calculations
8.10 How to validate
9.1 Creating parameters
9.2 Parameters in calculations
9.3 Using parameters with filters
9.4 Column selection parameters
9.5 Chart selection parameters
9.6 How to use parameters in the filter session
9.7 How to use parameters in calculated fields
9.8 How to use parameters in the reference line
1. Creating new parameters to apply on a filter
2. Passing parameters to filters to select columns
3. Passing parameters to filters to select charts
10.1 Dual axes graphs
10.3 Single and dual axes
10.4 Box plot
10.5 Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
10.6 Maps: tree and heat maps
10.7 Market basket analysis (MBA)
10.8 Using Show me
10.9 Text table and highlighted table
1. Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset
2. Perform market basket analysis (MBA) on the same dataset
11.1 Building and formatting a dashboard using size, objects, views, filters, and legends
11.2 Best practices for making creative as well as interactive dashboards using the actions
11.3 Creating stories, including the intro of story points
11.4 Creating as well as updating the story points
11.5 Adding catchy visuals in stories
11.6 Adding annotations with descriptions; dashboards and stories
11.7 What is dashboard?
11.8 Highlight actions, URL actions, and filter actions
11.9 Selecting and clearing values
11.10 Best practices to create dashboards
11.11 Dashboard examples; using Tableau workspace and Tableau interface
11.12 Learning about Tableau joins
11.13 Types of joins
11.14 Tableau field types
11.15 Saving as well as publishing data source
11.16 Live vs extract connection
11.17 Various file types
1. Create a Tableau dashboard view, include legends, objects, and filters
2. Make the dashboard interactive
3. Use visual effects, annotations, and descriptions to create and edit a story
12.1 Introduction to Tableau Prep
12.2 How Tableau Prep helps quickly combine join, shape, and clean data for analysis
12.3 Creation of smart examples with Tableau Prep
12.4 Getting deeper insights into the data with great visual experience
12.5 Making data preparation simpler and accessible
12.6 Integrating Tableau Prep with Tableau analytical workflow
12.7 Understanding the seamless process from data preparation to analysis with Tableau Prep
13.1 Introduction to R language
13.2 Applications and use cases of R
13.3 Deploying R on the Tableau platform
13.4 Learning R functions in Tableau
13.5 The integration of Tableau with Hadoop
1. Deploy R on Tableau
2. Create a line graph using R interface
3. Connect Tableau with Hadoop to extract data
Understanding the global covid-19 mortality rates
Analyze and develop a dashboard to understand the covid-19 global cases.Compare the global confirmed vs. death cases in a world map. Compare the country wise cases using logarithmic axes. Dashboard should display both a log axis chart and a default axis chart in an alternate interactive way. Create a parameter to dynamically view Top N WHO regions based on cumulative new cases and death cases ratio. Dashboard should have a drop down menu to view the WHO region wise data using a bar chart, line chart or a map as per user’s requirement.
Understand the UK bank customer data
Analyze and develop a dashboard to understand the customer data of a UK bank. Create an asymmetric drop down of Region with their respective customer names and their Balances with a gender wise color code. Region wise bar chart which displays the count of customers based on High and low balance. Create a parameter to let the users’ dynamically decide the limit value of balance which categorizes it into high and low. Include interactive filters for Job classifications and Highlighters for Region in the final dashboard.
Understand Financial Data
Create an interactive map to analyze the worldwide sales and profit. Include map layers and map styles to enhance the visualization. Interactive analysis to display the average gross sales of a product under each segment, allowing only one segment data to be displayed at once. Create a motion chart to compare the sales and profit through the years. Annotate the day wise profit line chart to indicate the peaks and also enable drop lines. Add go to URL actions in the final dashboard which directs the user to the respective countries Wikipedia page.
Understand Agriculture Data
Create interactive tree map to display district wise data. Tree maps should have state labels. On hovering on a particular state, the corresponding districts data are to be displayed. Add URL actions, which direct users’ to a Google search page of the selected crop. Web page is to be displayed on the final dashboard. Create a hierarchy of seasons, crop categories and the list of crops under each. Add highlighters for season. One major sheet in the final dashboard should be unaffected by any action applied. Use the view in this major sheet to filter data in the other. Using parameters color code the seasons with high yield and low yield based on its crop categories. Rank the crops based on their yield
1.1 Various types of databases
1.2 Introduction to Structured Query Language
1.3 Distinction between client server and file server databases
1.4 Understanding SQL Server Management Studio
1.5 SQL Table basics
1.6 Data types and functions
1.8 Authentication for Windows
1.9 Data control language
1.10 The identification of the keywords in T-SQL, such as Drop Table
2.1 Data Anomalies
2.2 Update Anomalies
2.3 Insertion Anomalies
2.4 Deletion Anomalies
2.5 Types of Dependencies
2.6 Functional Dependency
2.7 Fully functional dependency
2.8 Partial functional dependency
2.9 Transitive functional dependency
2.10 Multi-valued functional dependency
2.11 Decomposition of tables
2.12 Lossy decomposition
2.13 Lossless decomposition
2.14 What is Normalization?
2.15 First Normal Form
2.16 Second Normal Form
2.17 Third Normal Form
2.18 Boyce-Codd Normal Form(BCNF)
2.19 Fourth Normal Form
2.20 Entity-Relationship Model
2.21 Entity and Entity Set
2.22 Attributes and types of Attributes
2.23 Entity Sets
2.24 Relationship Sets
2.25 Degree of Relationship
2.26 Mapping Cardinalities, One-to-One, One-to-Many, Many-to-one, Many-to-many
2.27 Symbols used in E-R Notation
3.1 Introduction to relational databases
3.2 Fundamental concepts of relational rows, tables, and columns
3.3 Several operators (such as logical and relational), constraints, domains, indexes, stored procedures, primary and foreign keys
3.4 Understanding group functions
3.5 The unique key
4.1 Advanced concepts of SQL tables
4.2 SQL functions
4.3 Operators & queries
4.4 Table creation
4.5 Data retrieval from tables
4.6 Combining rows from tables using inner, outer, cross, and self joins
4.7 Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
4.8 Temporary table creation
4.9 Set operator rules
4.10 Table variables
5.1 Understanding SQL functions – what do they do?
5.2 Scalar functions
5.3 Aggregate functions
5.4 Functions that can be used on different datasets, such as numbers, characters, strings, and dates
5.5 Inline SQL functions
5.6 General functions
5.7 Duplicate functions
6.1 Understanding SQL subqueries, their rules
6.2 Statements and operators with which subqueries can be used
6.3 Using the set clause to modify subqueries
6.4 Understanding different types of subqueries, such as where, select, insert, update, delete, etc.
6.5 Methods to create and view subqueries
7.1 Learning SQL views
7.2 Methods of creating, using, altering, renaming, dropping, and modifying views
7.3 Understanding stored procedures and their key benefits
7.4 Working with stored procedures
7.5 Studying user-defined functions
7.6 Error handling
8.1 User-defined functions
8.2 Types of UDFs, such as scalar
8.3 Inline table value
8.4 Multi-statement table
8.5 Stored procedures and when to deploy them
8.6 What is rank function?
8.7 Triggers, and when to execute triggers?
9.1 SQL Server Management Studio
9.2 Using pivot in MS Excel and MS SQL Server
9.3 Differentiating between Char, Varchar, and NVarchar
9.4 XL path, indexes and their creation
9.5 Records grouping, advantages, searching, sorting, modifying data
9.6 Clustered indexes creation
9.7 Use of indexes to cover queries
9.8 Common table expressions
9.9 Index guidelines
10.1 Creating Transact-SQL queries
10.2 Querying multiple tables using joins
10.3 Implementing functions and aggregating data
10.4 Modifying data
10.5 Determining the results of DDL statements on supplied tables and data
10.6 Constructing DML statements using the output statement
11.1 Querying data using subqueries and APPLY
11.2 Querying data using table expressions
11.3 Grouping and pivoting data using queries
11.4 Querying temporal data and non-relational data
11.5 Constructing recursive table expressions to meet business requirements
11.6 Using windowing functions to group
11.7 Rank the results of a query
12.1 Creating database programmability objects by using T-SQL
12.2 Implementing error handling and transactions
12.3 Implementing transaction control in conjunction with error handling in stored procedures
12.4 Implementing data types and NULL
13.1 Designing and implementing relational database schema
13.2 Designing and implementing indexes
13.3 Learning to compare between indexed and included columns
13.4 Implementing clustered index
13.5 Designing and deploying views
13.6 Column store views
14.1 Explaining foreign key constraints
14.2 Using T-SQL statements
14.3 Usage of Data Manipulation Language (DML)
14.4 Designing the components of stored procedures
14.5 Implementing input and output parameters
14.6 Applying error handling
14.7 Executing control logic in stored procedures
14.8 Designing trigger logic, DDL triggers, etc.
15.1 Applying transactions
15.2 Using the transaction behavior to identify DML statements
15.3 Learning about implicit and explicit transactions
15.4 Isolation levels management
15.5 Understanding concurrency and locking behavior
15.6 Using memory-optimized tables
16.1 Accuracy of statistics
16.2 Formulating statistics maintenance tasks
16.3 Dynamic management objects management
16.4 Identifying missing indexes
16.5 Examining and troubleshooting query plans
16.6 Consolidating the overlapping indexes
16.7 The performance management of database instances
16.8 SQL server performance monitoring
17.1 Correlated Subquery, Grouping Sets, Rollup, Cube
18.1 Performance Tuning and Optimizing SQL Databases
18.2 Querying Data with Transact-SQL
Writing Complex Subqueries
In this project, you will be working with SQL subqueries and utilizing them in various scenarios. You will learn to use IN or NOT IN, ANY or ALL, EXISTS or NOT EXISTS, and other major queries. You will be required to access and manipulate datasets, operate and control statements in SQL, execute queries in SQL against databases.
Querying a Large Relational Database
This project is about how to get details about customers by querying the database. You will be working with Table basics and data types, various SQL operators, and SQL functions. The project will require you to download a database and restore it on the server, query the database for customer details and sales information.
Relational Database Design
In this project, you will learn to convert a relational design that has enlisted within its various users, user roles, user accounts, and their statuses into a table in SQL Server. You will have to define relations/attributes, primary keys, and create respective foreign keys with at least two rows in each of the tables.
Installation and introduction to SAS, how to get started with SAS, understanding different SAS windows, how to work with data sets, various SAS windows like output, search, editor, log and explorer and understanding the SAS functions, which are various library types and programming files
How to import and export raw data files, how to read and subset the data sets, different statements like SET, MERGE and WHERE
Hands-on Exercise: How to import the Excel file in the workspace and how to read data and export the workspace to save data
Different SAS operators like logical, comparison and arithmetic, deploying different SAS functions like Character, Numeric, Is Null, Contains, Like and Input/Output, along with the conditional statements like If/Else, Do While, Do Until and so on
Hands-on Exercise: Performing operations using the SAS functions and logical and arithmetic operations
Understanding about input buffer, PDV (backend) and learning what is Missover
Defining and using KEEP and DROP statements, apply these statements and formats and labels in SAS
Hands-on Exercise: Use KEEP and DROP statements
Understanding the delimiter, dataline rules, DLM, delimiter DSD, raw data files and execution and list input for standard data
Hands-on Exercise: Use delimiter rules on raw data files
Various SAS standard procedures built-in for popular programs: PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, PROC CORR, etc.
Hands-on Exercise: Use SORT, FREQ, SUMMARY, EXPORT and other procedures
Reading standard and non-standard numeric inputs with formatted inputs, column pointer controls, controlling while a record loads, line pointer control/absolute line pointer control, single trailing, multiple IN and OUT statements, dataline statement and rules, list input method and comparing single trailing and double trailing
Hands-on Exercise: Read standard and non-standard numeric inputs with formatted inputs, control while a record loads, control a line pointer and write multiple IN and OUT statements
SAS Format statements: standard and user-written, associating a format with a variable, working with SAS Format, deploying it on PROC data sets and comparing ATTRIB and Format statements
Hands-on Exercise: Format a variable, deploy format rule on PROC data set and use ATTRIB statement
Understanding PROC GCHART, various graphs, bar charts: pie, bar and 3D and plotting variables with PROC GPLOT
Hands-on Exercise: Plot graphs using PROC GPLOT and display charts using PROC GCHART
SAS advanced data discovery and visualization, point-and-click analytics capabilities and powerful reporting tools
Character functions, numeric functions and converting variable type
Hands-on Exercise: Use functions in data transformation
Introduction to ODS, data optimization and how to generate files (rtf, pdf, html and doc) using SAS
Hands-on Exercise: Optimize data and generate rtf, pdf, html and doc files
Macro Syntax, macro variables, positional parameters in a macro and macro step
Hands-on Exercise: Write a macro and use positional parameters
SQL statements in SAS, SELECT, CASE, JOIN and UNION and sorting data
Hands-on Exercise: Create SQL query to select and add a condition and use a CASE in select query
Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval and descriptive statistics
Hands-on Exercise: Use web UI to do statistical operations
Report enhancement, global statements, user-defined formats, PROC SORT, ODS destinations, ODS listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT and PROC PRINT
Hands-on Exercise: Use PROC SORT to sort the results, list ODS, find mean using PROC Means and print using PROC PRINT
Categorization of Patients Based on the Count of Drugs for Their Therapy
This project aims to find out descriptive statistics and subset for specific clinical data problems. It will give them brief insight about Base SAS procedures and data steps.
Build Revenue Projections Reports
You will be working with the SAS data analytics and business intelligence tool. You will get to work on the data entered in a business enterprise setup and will aggregate, retrieve, and manage that data. Create insightful reports and graphs and come up with statistical and mathematical analysis to predict revenue projection.
Impact of Pre-paid Plans on the Preferences of Investors
This project aims to find the most impacting factors in the preferences of the pre-paid model. The project also identifies which variables are highly correlated with impacting factors. In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale.
k-means cluster Analysis on Iris Dataset
In this project, you will be required to do k-means cluster analysis on an Iris dataset to predict the class of a flower using the dimensions of its petals.
How does Qlik Sense vary from QlikView, the need for self-service Business Intelligence/Business Analytics tools, Qlik Sense data discovery, intuitive tool for dynamic dashboards and personalized reports and the installation of Qlik Sense and Qlik Sense Desktop
Hands-on Exercise: Install Qlik Sense and Qlik Sense Desktop
Drag-and-drop visualization, Qlik Data indexing engine, data dimensions relationships, connect to multiple data sources, creating your own dashboards, data visualization, visual analytics and the ease of collaboration
Hands-on Exercise: Connect to a database or load data from an Excel file and create a dashboard
Understand data modeling, best practices, turning data columns into rows, converting data rows into fields, hierarchical-level data loading, loading new or updated data from database, using a common field to combine data from two tables and handling data inconsistencies
Hands-on Exercise: Turn data columns into rows, convert data rows into fields, load the data in hierarchical level, load new or updated data from database and use a common field to combine data from two tables
Qlik Sense data architecture, understanding QVD layer, converting QlikView files to Qlik Sense files and working on synthetic keys and circular references
Hands-on Exercise: Convert QlikView files to Qlik Sense files and resolve synthetic keys and circular references
Qlik Sense star schema, link table, dimensions table, master calendar, QVD files and optimizing data modeling
Hands-on Exercise: Create a Qlik Sense star schema, create link table, dimensions table, master calendar and QVD files
Qlik Sense enterprise class tools, Qlik Sense custom app, embedding visuals, rapid development, powerful open APIs, enterprise-class architecture, Big Data integration, enterprise security and elastic scaling
Learning about Qlik Sense visualization tools, charts and maps creation, rich data storytelling and sharing analysis visually with compelling visualizations
Hands-on Exercise: Create charts and maps, create a story around dataset and share analysis
Understanding set analysis in Qlik Sense, various parts of a set expression like identifiers, operators, modifiers and comparative analysis
Hands-on Exercise: Do Set Analysis in Qlik Sense, use set expression like identifiers, operators, modifiers and comparative analysis
Learning about set analysis which is a way of defining a set of data values different from normal set, deploying comparison sets and point-in-time analysis
Hands-on Exercise: Deploy comparison sets and perform point-in-time analysis
Introduction to various charts in Qlik Sense like line chart, bar chart, pie chart, table chart and pivot table chart and the characteristics of various charts
Hands-on Exercise: Plot charts in Qlik Sense like line chart, bar chart, pie chart, table chart and pivot table chart
Understanding what is a KPI chart, gauge chart, scatter plots chart and map chart/geo map
Hands-on Exercise: Plot a KPI chart, gauge chart, scatter plots chart and map chart/geo map
Introduction to the Qlik Sense Master Library, its benefits, distinct features and user-friendly applications
Hands-on Exercise: Explore and use Qlik Sense Master Library
Understanding how to do storytelling in Qlik Sense and the creation of storytelling and story playback
Hands-on Exercise: Use the storytelling feature of Qlik Sense, create a story and playback the story
Understanding mashups in Qlik Sense, creating a single graphical interface from more than one sources, deploying the mashups flowchart, testing of mashups and the various mashup scenarios like simple and normal
Hands-on Exercise: Create a single graphical interface from more than one sources, deploy the mashups flowchart and test mashups
Understanding the Qlik Sense Extension, working with it, various templates in Qlik Sense Extension, testing of it, making Hello World dynamic and learning how it works and adding a preview image
Hands-on Exercise: Work with Qlik Sense Extension, use a template in Qlik Sense Extension and test it, make Hello World dynamic and add a preview image
Various security aspects of Qlik Sense, content security, security rules, various components of security rules and understanding data reductions and dynamic data reductions and the user access workflow
Hands-on Exercise: Create security rules in Qlik Sense and understand data reductions and dynamic data reductions and the user access workflow
Objective: This project involves working with the Qlik Sense dashboard that displays the sales details whether order-wise, year-wise, customer-wise sales or product-wise sales and so on, doing comparative analysis, rolling six months analysis that should be displaying the trend of sales and placing the worksheets in a user story and publishing.
Domain: Data Analytics
Objective: To see the current values of salaries in one column and historical values in another cell in a chart that would contain a bar chart and a trend chart
Objective: Visual Mapping between the vaccination rate and measles outbreak
Introduction to Excel spreadsheet, learning to enter data, filling of series and custom fill list, editing and deleting fields.
Learning about relative and absolute referencing, the concept of relative formulae, the issues in relative formulae, creating of absolute and mixed references and various other formulae.
Creating names range, using names in new formulae, working with the name box, selecting range, names from a selection, pasting names in formulae, selecting names and working with Name Manager.
the various logical functions in Excel, the If function for calculating values and displaying text, nested If functions, VLookUp and IFError functions.
Learning about conditional formatting, the options for formatting cells, various operations with icon sets, data bars and color scales, creating and modifying sparklines.
multi-level drop down validation, restricting value from list only, learning about error messages and cell drop down.
Introduction to the various formulae in Excel like Sum, SumIF & SumIFs, Count, CountA, CountIF and CountBlank, Networkdays, Networkdays International, Today & Now function, Trim (Eliminating undesirable spaces), Concatenate (Consolidating columns)
Introduction to dynamic table in Excel, data conversion, table conversion, tables for charts and VLOOKUP.
Sorting in Excel, various types of sorting including, alphabetical, numerical, row, multiple column, working with paste special, hyperlinking and using subtotal.
The concept of data filtering, understanding compound filter and its creation, removing of filter, using custom filter and multiple value filters, working with wildcards.
Creation of Charts in Excel, performing operations in embedded chart, modifying, resizing, and dragging of chart.
Introduction to the various types of charting techniques, creating titles for charts, axes, learning about data labels, displaying data tables, modifying axes, displaying gridlines and inserting trendlines, textbox insertion in a chart, creating a 2-axis chart, creating combination chart.
The concept of Pivot tables in Excel, report filtering, shell creation, working with Pivot for calculations, formatting of reports, dynamic range assigning, the slicers and creating of slicers.
Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.
Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros.
In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.
Learning about the concepts of workbooks and worksheets in Excel, protection of macro codes, range coding, declaring a variable, the concept of Pivot Table in VBA, introduction to arrays, user forms, getting to know how to work with databases within Excel.
Learning how the If condition works and knowing how to apply it in various scenarios, working with multiple Ifs in Macro.
Understanding the concept of looping, deploying looping in VBA Macros.
Studying about debugging in VBA, the various steps of debugging like running, breaking, resetting, understanding breakpoints and way to mark it, the code for debugging and code commenting.
The concept of message box in VBA, learning to create the message box, various types of message boxes, the IF condition as related to message boxes.
Mastering the various tasks and functions using VBA, understanding data separation, auto filtering, formatting of report, combining multiple sheets into one, merging multiple files together.
Introduction to powerful data visualization with Excel Dashboard, important points to consider while designing the dashboards like loading the data, managing data and linking the data to tables and charts, creating Reports using dashboard features.
Learning to create charts in Excel, the various charts available, the steps to successfully build a chart, personalization of charts, formatting and updating features, various special charts for Excel dashboards, understanding how to choose the right chart for the right data.
Creation of Pivot Tables in Excel, learning to change the Pivot Table layout, generating Reports, the methodology of grouping and ungrouping of data.
Learning to create Dashboards, the various rules to follow while creating Dashboards, creation of dynamic dashboards, knowing what is data layout, introduction to thermometer chart and its creation, how to use alerts in the Dashboard setup.
How to insert a Scroll bar to a data window?, Concept of Option buttons in a chart, Use of combo box drop-down, List box control Usage, How to use Checkbox Control?
Understanding data quality issues in Excel, linking of data, consolidating and merging data, working with dashboards for Excel Pivot Tables.
Project – if Function
Data – Employee
Problem Statement – It describes about if function and how to implement this if function. It includes following actions:
Calculates Bonus for all employee at 10% of their salary using if Function, Rate the salesman based on the sales and the rating scale., Find the number of times “3” is repeated in the table and find the number of values greater than 5 using Count Function, Uses of Operators and nested if function
What is statistics?, How is this useful, What is this course for
Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data
Descriptive statistics, Let us understand some terms in statistics, Variable
Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots
What is probability?, Set & rules of probability, Bayes Theorem
Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution
Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling
Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables
Project â€“ Data Analysis Project
Data â€“ Sales
Problem Statement â€“ It includes the following actions:
Understand the business solutions, Discussion with the warehouse team, Data Collection & Storage, Data Cleaning, Build a Hypothesis Tree around the business problem, Produce the final result.
Free Career Counselling
This is a comprehensive Data Analytics Online Certification course that is designed to clear multiple certifications viz.
The complete Data Analytics courses in Hyderabad are created by industry experts for professionals to get the top jobs in the best organizations. Further, Data Analytics online courses includes real-time projects and case studies that are highly valuable.
Upon completion of this online training you will have quizzes that will help you prepare for the respective certification exams and score top marks.
The Intellipaat Certification is awarded upon successfully completing the project work and after these projects are reviewed by experts. The Intellipaat certification is recognized in some of the biggest companies like Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony, Ericsson among many others. Also, you will be awarded the Data Analytics certification from two of the largest organizations in the world, IBM and Microsoft.
Our Alumni works at top 3000+ companies
Intellipaat Data Analytics Course in Hyderabad is an industry-designed course, designed for you to fast-track your career in the domain of data analytics. If you don’t want to get into the nitty-gritty of programming and spend lengthy hours in coding necessary for becoming a Data Analyst, then Intellipaat’s courses on Data Analytics is for you.
The online Data Analytics courses involves the following:
A career in the Data Analytics domain is not just a good career option but one of the most popular careers today. You can find jobs in this domain across a diverse range of industries and companies around the globe by completing Master’s in Data Analytics.
As per the Bureau of Labor Statistics, the estimated growth rate for Data Analytics professionals will shoot up by 23% by the year 2026.
To become a Data Analyst, you must have the following qualifications:
For becoming a Data Analyst, you should meet the following criteria:
You can attain all the necessary skills, gain real-time experience, and receive a certification with the help of Intellipaat’s Data Analyst Master’s program.
Having a college degree in the field of mathematics, probability, or computer science can definitely be beneficial. However, it is not mandatory for you to have the same. The main requirement of becoming a Data Analyst is that you need to possess the necessary skills in this domain. So, having a degree can help you immensely, however, it is still a secondary requirement.
Intellipaat offers self-paced training to those who wish to learn at their own pace. This training also gives you benefits like query resolution through email, live sessions with trainers, round-the-clock support, and access to the learning modules on LMS for entire lifetime. Also, you will get the latest version of the course material at no additional cost.
Intellipaat’s self-paced training is priced 75 percent lesser compared to the online instructor-led training. If you face any problems while learning, our team can always arrange a virtual live class with the trainers as well.
Intellipaat provides you the most updated, relevant, and high-value projects as part of this training program. This way, you can implement the knowledge and skills that you have acquired in a real-world industry setup. All our trainings come with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on exciting projects in the domain of latest technologies, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equivalent to 6 months of rigorous industry experience.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.