Data Analytics Course

Learn how to do data analysis, data transformation and other important data analysis functions. Basic fundamental of Python and learn how to do data analytics using the same.

Module 2 – Data wrangling with SQL

2.1 Introduction to SQL
2.2 Database normalization
2.3 Entity-relationship model
2.4 SQL operators
2.5 Join, tables, and variables
2.6 SQL functions
2.7 Subqueries
2.8 SQL functions, views, and stored procedures
2.9 User-defined functions
2.10 SQL performance and optimization
2.11 Advanced concepts

Correlated subquery
Grouping sets

Module 3 – Presto

3.1 Introduction to Presto
3.2 Writing Queries in Presto on large data sets.
3.3 Data Transformation using Presto

Module 4 – Introduction to Data Science & Statistics

4.1 Descriptive Statistics

Mean
Median
Mode
Tables
Charts

4.2 Introduction to Probability

Probability in Business Analytics

4.3 Probability Distributions

Binomial distribution
Poisson distribution
Normal distribution

4.4 Hypothesis Testing and Estimation

Hypothesis testing
Estimation

4.5 Goodness of Fit

How the sample adequately fits a distribution from an observational set

Module 5 – Business Problem Solving, Insights and Storytelling

5.1 Business domains

Finance
Marketing
Retail
Supply Chain

5.2 Understanding the business problem and formulating hypotheses
5.3 Exploratory data analysis
5.4 Data storytelling: Narrate stories in a memorable way
5.5 Project on deriving business insights and storytelling

Module 6 – Optimization Techniques

6.1 Linear programming
6.2 Goal programming
6.3 Integer programming
6.4 Mixed integer programming
6.5 Distribution and network models

Module 7 - KNIME

7.1 Introduction to KNIME
7.2 Working with data in KNIME
7.3 Loops in KNiME
7.4 Webscraping in KNIME
7.5 Hyperparameter optimization in KNIME
7.6 Hyperparameter optimization for Machine Learning Models using loops in KNIME
7.7 Feature Selection in KNIME

Module 8 – Statistics & Machine Learning using R Programming

8.1 Programming with R
8.2 Advance Statistics

ANOVA
Regression analysis
Dimension reduction techniques

8.3 Data Mining

Supervised and unsupervised learning
Clustering
Market Basket Analysis
Decision trees
Random forest
Neural networks

Module 9 – Predictive Modeling

9.1 Multiple linear regression
9.2 Logistic regression
9.3 Linear discriminant analysis

Module 10 – Time-Series Forecasting

10.1 Introduction to time-series
10.2 Correlation
10.3 Forecasting
10.4 Autoregressive models

Module 11 – Feature Engineering

11.1 Handling unstructured data
11.2 Machine Learning algorithms
11.3 Bias variance trade-off
11.4 Handling unbalanced data
11.5 Boosting
11.6 Model validation

Module 12 – Advance Machine Learning Techniques

12.1 Hyper parameter optimization
12.2 Advance Machine Learning Libraries – XGBoost
12.3 Solving Problems on Kaggle

Module 13 - Data Science Execution Strategy

13.1 Framework to Data Science strategy
13.2 Mapping Data Science with data architecture strategy
13.3 Executing Data Science strategy

Module 14 – Business Case Studies

14.1 Marketing & retail analytics
14.2 Social analytics
14.3 Logistics & supply chain
14.4 Financial analytics

Module 15 – Visualization using Power BI

15.1 Introduction to Power BI
15.2 Data Extraction
15.3 Data Transformation – Shaping & Combining Data
15.4 Data Modelling & DAX
15.5 Data Visualisation with analytics
15.6 Power BI Service (Cloud), Q&A, and Data Insights
15.7 Power BI Settings, Administration & Direct Connectivity
15.8 Embedded Power BI with API & Power BI
15.9 Power BI Advance & Power BI Premium

Module 16 – Data Science Capstone Project

In the Data Science & Business Analytics Capstone project, you will use all the knowledge and skills you have acquired throughout this advanced certification program and get real world Industry project exposure.

Free Career Counselling

We are happy to help you 24/7

Project Work

Projects will be a part of your Data Analyst Master’s program to consolidate your learning. It will ensure that you have real-world experience in Data Analytics.

Predicting class of a flower with petals data

This project is made to predict the class of a flower. By analyzing the data about the petal and their dimensions, the class of the flower would be determined using a trained Machine Learning model that has optimum accuracy.

Data Analytics on Census dataset

The project is meant to analyze and predict whether an individual has an annual income of more than Rs 50000 or less using various machine learning techniques, some of which are supervised and unsupervised learning.

Fraud Detection

The project will be creating a model which would help in detecting Credit Card fraud using Machine Learning subcategories like supervised, unsupervised, and reinforcement learning techniques like Neural Network and Random Forest.

Analyzing impact factors of the pre-paid model in telecom

The project aims to find the most impacting factors in the preferences of the pre-paid model and to identify which all are the variables highly correlated with impacting factors. The goal of this project is to find the factors that have the maximum impact on the preferences of the pre-paid model. It also aims at identifying the variables that are highly correlated with these impacting factors.Read More

Pokemon Analysis

Select a pokemon whose primary type is “Grass”, secondary type is “Poison” with the maximum speed. It creates a model that predicts whether the selected pokemon is Legendary or not using Machine Learning and data analysis techniques.

Stock Market Analysis

This project focuses on Machine Learning by creating a predictive data model to predict the future stock prices. It also analyzes the current trends in the stock market using algorithms like Linear Regression and k-Nearest Neighbors.

Comment Identity & Classification

Work on the comment dataset of Yelp to build a multi-headed model that can identify & categorize various types of toxic comments (obscenity, insults, threats etc.). It must allow the user to find and select the required type

Book Recommender

This project aims at building a book recommendation engine based on past reading and buying history of an individual. It will analyze the dataset using Machine Learning techniques, R programming and its various libraries.

Loan Approval Prediction

The project aims to analyze whether the applied loan would be approved or not by identifying the variables correlated with impacting factors. It is predicted using techniques such as Logistic Regression, Decision Tree and Random Forest.

Recommendation Engine using Netflix prize dataset

The recommendation engine is built using Netflix prize dataset that will collect the data from various sources of a user, anticipate their preferences and will recommend movies according to the user’s behavior and choices made earlier.

Career Transition

Abhilasha Kumari

Executive | India

25% Salary Hike

After developing an interest in Data Analysis, I enrolled in the training. I am glad I got a job in this domain and shift from a non-tech role to a Data Analyst. Also, I am now much more confident in data analysis than before.

Associate , Client support

Executive

Akinola Obafemi Sanyaolu

Non-Tech to Data Analyst

Intellipaat has guided me to take the right course where I learned the latest tools and technologies from R to Tableau, SAS, MS Excel, and SQL allowing me to switch my career from sales to a tech role.

Project Manager

Data Analyst

Melwin Rodrigues

Data Scientist | India

Non-Tech to Data Scientist

Becoming a Data Scientist from a Customer Service Agent was possible only due to expert guidance by Intellipaat trainers. Even after working for 10 years in customer care, I am a Data scientist today.

Customer Service Agent

Data Scientist

Shehzin Mulla

Marketing Data Analyst | India

Salary Hike

Post the course, I was able to shift from a Data Analyst to a Marketing Data Analyst with a 35% salary hike. I gained a deep understanding of technical skills, especially in analytics. I can’t thank you enough, Intellipaat

Data Analyst

Marketing Data Analyst

Vishal Sharma

Senior Analyst | India

30 Salary Hike & Internal Role Transition

I was able to successfully make an internal transition from a Business Analyst to a Senior Analyst. The training has helped me effectively execute the projects in my organization. I am glad to have chosen Intellipaat.

Business Analyst

Senior Analyst

Ankit Kumar

Data Scientist | Delhi