Advanced Certification in Data Analytics for Business

Learning about various Data Manipulation and Data Handling techniques with the help of the following: 

1. MS-Excel – 

• Excel Fundamentals 

1. 1. Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
   2. Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
   3. VBA Macros, Ranges and Worksheet in VBA 
   4. IF conditions, loops, Debugging, etc.

• Excel For Data Analytics 

1. 1. Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

• Data Visualization with Excel

1. 1. Charts, Pie charts, Scatter and bubble charts
   2. Bar charts, Column charts, Line charts, Maps
   3. Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

2. Python

• Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
• Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
• Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
• Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e machine learning models, etc.
• Case Study – The culmination of all the above concepts with real-world problem statements for better understanding.

1. SQL Basics – 

• Fundamentals of Structured Query Language
• SQL Tables, Joins, Variables 

2. Advanced SQL –  

• SQL Functions, Subqueries, Rules, Views
• Nested Queries, string functions, pattern matching
• Mathematical functions, Date-time functions, etc. 

3. Deep Dive into User Defined Functions

• Types of UDFs, Inline table value, multi-statement table. 
• Stored procedures, rank function, SQL ROLLUP, etc. 

4. SQL Optimization and Performance

• Record grouping, searching, sorting, etc. 
• Clustered indexes, common table expressions.

Hands-on exercise:

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts). 

1. What is Data Science – Significance of Data Science in today’s data-driven world, its applications of the lifecycle, and its components Introduction to R programming and RStudio

Case Study 

This case study will cover the following concepts: 

• Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases.

2. Data Exploration – 

• Introduction to data exploration
• Importing and exporting data to/from external sources
• What are data exploratory analysis and data importing?
• DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Case Study

This case study will cover the following concepts:

1. Accessing individual elements of customer churn data.
2. Modifying and extracting results from the dataset using user-defined functions in R.

3. Data Manipulation 

• Need for data manipulation
• Introduction to the dplyr package
• 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
• Combining different functions with the pipe operator and implementing SQL

Case Study 

This case study will cover the following concepts:

1. Implementing dplyr.
2. Performing various operations for manipulating data and storing it.

1. Descriptive Statistics – 

• Measure of central tendency, measure of spread, five points summary, etc. 

2. Probability 

• Probability Distributions, Probability in Business Analytics
• Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem. 

3. Inferential Statistics –  

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

4. Case Study

This case study will cover the following concepts:

• Building a statistical analysis model that uses quantification, representations, and experimental data
• Reviewing, analyzing, and drawing conclusions from the data

1. Business Domains – Learn about various business domains and understand how one differs from the other. 

• Finance
• Marketing
• Retail
• Supply Chain

2. Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations. 

3. Exploratory Data Analysis to Gather insights – Learn about the exploratory data analysis and how it enables a fool proof producer of actionable insights. 

4. Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in simple relatable format that makes it easier to understand and recall. 

5. Case Study

This case study will cover the following concepts:

1. Create actionable insights from raw unstructured data to solve real world business problems.

1. Regression

1. Linear & Logistic Regression, Exploratory Data Analysis, Handling unbalanced data.
2. Dimensionality reduction techniques, Model validation, Bias variance trade-off.

2. Trees(Decision Tree And Random Forest)

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.
3. Implementing predictive analytics by describing data.
4. Explaining the relationship between one dependent binary variable and one or more binary variables.
5. Using glm() to build a model, with ‘Churn’ as the dependent variable.
6. Implementing random forest for both regression and classification problems.
7. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees.
8. Using ROCR for performance metrics.
9. k means clustering, Applications of Unsupervised (Market Basket Analysis, Segmentation).

3. Case Study

This case study will cover the following concepts:

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.
3. Hyperparameter optimization and Deploying association analysis as a rule-based Machine Learning method.
4. Identifying strong rules discovered in databases with measures based on interesting discoveries.

1. Introduction to KNIME – learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc. 

2. Working with data in KNIME – Learn about creating workflows, loading datasets in KNIME, etc.

3. Loops in KNIME – Learn about the loops in KNIME that enables efficient data transformation in KNIME

4. Web scraping in KNIME – Learn about techniques in KNIME that enable web scraping to collect data directly from the web. 

5. Feature Selection, Hyperparameter optimization in KNIME – Learn about hyperparameter optimization, feature selection in KNIME that will enable efficient machine learning models.

6. Case Study:

This case study will cover the following concepts:

1. Using KNIME to create end to end machine learning models with various algorithms like linear regression, logistic regression, decision tree, random forest, etc.

1. Introduction to Machine learning 

• Supervised, Unsupervised learning.
• Introduction to machine learning libraries in R programming. 

2. Regression 

• Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
• How to train the model in a regression problem. 
• How to evaluate the model for a regression problem. 
• How to optimize the efficiency of the regression model. 

3. Classification 

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model. 

4. Clustering 

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.

5. Supervised Learning – Linear regression, logistic regression, decision tree, random forest, support vector machine, gradient descent, K-nearest neighbor, time series forecasting, etc. 

6. Unsupervised Learning – K-means clustering, dimensionality reduction, linear discriminant analysis, principal component analysis, etc. 

7. Performance Metrics – Classification reports, recall, precision, f-support, confusion matrix, true positive, false positive, true negative, false negative, r2, adjusted r2, mean squared error, etc.

8. Case Study:

This case study will cover the following concepts:

• Predictive machine learning models in a supervised and unsupervised learning setup using algorithms like linear regression, logistic regression, random forest, decision tree, k-means, KNN, time series forecasting, LDA, PCA, etc.

1. Power BI Basics

• Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI. 
• SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
• Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

2. DAX 

• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

3. Data Visualization with Analytics  

• Slicers, filters, Drill Down Reports
• Power BI Query, Q & A and Data Insights
• Power BI Settings, Administration and Direct Connectivity 
• Embedded Power BI API and Power BI Mobile 
• Power BI Advance and Power BI Premium

Hands-on Exercise:

This case study will cover the following concepts:

• Creating a dashboard to depict actionable insights in sales data.

1. Introduction to Presto – Learn about presto, what it is used for, and how the distributed SQL query engine works towards analytics and big data. 

2. Data Transformation using Presto – 

1. Combined ETL and AI workloads. 
2. Querying Data from the source, etc. 

1. Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement. 

2. Approach for the Solution – Creating various statistical insights based solutions to approach the problem will guide your learnings to finish a project from scratch. 

3. Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements. 

4. Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any. 

5. Gathering Actionable insights – You will learn about how a problem’s solution isn’t just creating a machine learning model, the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

1. Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same. 

2. Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next given time period(weeks, months, years, etc.)

3. Census – After studying the population data, we will gather insights and through predictive modeling try to create actionable insights on the same, it could be average income of an individual, or most likely profession, etc. 

4. Predictive Modeling – Various case studies on categorical and continuous data, to create predictive models that will predict specific outcomes based on the business problems. 

5. HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing. 

6. Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems. 

7. Housing – A case study that will give you insight into how real estate firms can narrow down on the pricing, customer choices, etc. using various predictive modeling techniques. 

8. Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.

9. Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques. 

10. Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in early detection of various diseases. 

11. Image Classification – The case study will entail working with image data, and how simple machine learning techniques can be useful to recognize image data at the behest of well trained and optimized machine learning models.