Data Analytics Master Course

Do you love huge or big data? Data handling was your area of interest in schools? Then this course is best for you to work with huge data to get some hidden success mantra which provide solutions to businesses to grow in their domains. You are welcome if you have analytical thinking with interest in programming and 10+2 (intermediate) to make businesses succeed.

04 Weeks: Module 1

Foundations – Basics of Data Analytics

Part 1 – Introduction

• Introduction to Data Analytics
• Data Analytics in Real Life
• Data Analysis vs Data Analytics
• Common Tools and Technologies
• Data and Its Importance
• Common Steps for Data Analysis

Part II – Math Refresher

• Useful Concepts of Math
• Use of Algebra, Geometry, and Calculus in Data Analysis!
• Data Table, Charts, Graphs etc.

Part III – Statistics

• Statistics in Data Analysis
• Types of Statistics
• Types of Data
• Categorical Data
• Numerical Data
• Association between Categorical & Numerical Variables
• Correlation and Covariance
• Hypothesis Testing
• Advanced Statistical Techniques
  • Multivariate Regression
  • Classification Problems
  • Predictive Analytics
  • Text Analytics

Part IV – Probability

• Rules of Probability
• Conditional Probability
• Random Variables
• Distributions
• Combinatorics
• Bayes Theorem
• Bayesian Inference

Part V – Data Analysis and Visualization in Excel

• Functions in Excel
• Data Analysis
• Pivot Tables and Lookup
• Interactive charts
• What-if-analysis, GOAL SEEK
• Linking Multiple Sheets
• Sub Total Reports, Auto Filter
• Password Protecting Worksheets
• Dashboard
• Record a Macro/VBA

Part VI – Data Analytics Essentials of SQL

• Introduction to DBMS
• ER diagram
• Schema design
• Key constraints & basics of normalization
• Joins
• Subqueries involving joins & aggregations
• Sorting
• Independent subqueries
• Correlated subqueries
• Analytic functions
• Set operations
• Grouping and filtering

10 Weeks: Module 2

Data Analytics Practical

Part 1 – Data Analysis with Python

• Installing and Exploring Python
• Anaconda
• Jupyter Notebook
• NumPy
• Python Pandas
• Matplotlib
• SciPy
• Django Framework

Part 2 – Use of R Language in Data Analysis

• Exploring R Programming Fundamentals
• Data Manipulation in R
• Data Modelling in R
• Variance, Linear Regression, and Logistic Regression in R
• RStudio
• Data Visualization with R
• Project in R

06 Weeks: Module 3

Data Visualization and Insights

Part 1 – Exploring Power BI

• Introduction to Data Visualization
• Introduction to Power BI
• Overview of the Rhyme Interface
• Importing the Data
• Fixing the Column Names
• Transforming the Data
• Creating Reports
• Defaults by Education Level
• Defaults by States
• Defaulter Ratio and Slicing the Data
• Project in Power BI

Part 2 – Exploring Tableau

• Introduction to Tableau
• Basic charts and dashboard
• Descriptive Statistics, Dimensions and Measures
• Visual analytics: Storytelling through data
• Dashboard design & principles
• Advanced design components – Improving the dashboards
• Special types of chart
• Integrate Tableau with Google Sheets and other data sources
• Project in Tableau

04 Weeks: Module 4

Domain Exposure

• Analytics for Marketing & Retail
• Analytics for Web & Social Media
• Analytics for Finance & Risk
• Analytics for Supply Chain & Logistics

You will be working on ‘Data Analytics Project from Scratch’ project with our data analytics expert so that you can prepare yourself to work in the most competitive industry of data analytics after completion of this analytics course.

Students who successfully completed this course are working as Senior Data Analyst, Python Data Analyst, R Data Analyst, R Developer, Python Expert, Business Analyst, SQL Expert, Excel Expert, Power BI Expert, Tableau Professional etc.

Interested in this course take a look at some of the similar courses:

• Data Analytics Standard Course: 02 Months
• Data Analytics Premium Course: 04 Months