Professional Data Analytics Training Program
The Professional Data Analytics Training Program is designed to teach you the core concepts of data collection, data cleaning, visualization, and analysis.
In this course, you will learn the practical use of tools such as Excel, SQL, and basic analytics platforms. By working on real-world datasets, you will develop the ability to generate business insights and support data-driven decision-making.
This course is ideal for students who want to build a career in the data field.
After completing the program, you can apply for roles such as Data Analyst, Business Analyst, or Junior Data Professional. Placement assistance is also available.
Curriculum
1.1 Introduction to Data Analytics
1.2 Types of data and data analyst roles
1.3 Excel interface, rows, columns and tables
1.4 Formulas: IF, SUMIF, COUNTIF, XLOOKUP
1.5 Sorting, filtering and data cleaning
1.6 Pivot Tables, Pivot Charts and dashboards
1.7 Basic data visualization
2.1 Database, tables, rows and columns
2.2 SELECT, WHERE, ORDER BY, DISTINCT
2.3 Aggregate functions and GROUP BY
2.4 HAVING, CASE WHEN
2.5 Primary key and foreign key
2.6 INNER, LEFT, RIGHT joins
2.7 Subqueries, CTEs and basic window functions
2.8 NULL handling and data cleaning
3.1 Python basics, variables, data types and loops
3.2 Lists, dictionaries, functions and file handling
3.3 NumPy fundamentals
3.4 Pandas Series and DataFrames
3.5 Reading Excel, CSV and SQL data
3.6 Missing values, duplicates and data cleaning
3.7 Filtering, grouping and merging datasets
3.8 Matplotlib and Seaborn basics
4.1 Mean, median, mode and percentiles
4.2 Variance and standard deviation
4.3 Probability fundamentals
4.4 Sampling and distributions
4.5 Correlation and outliers
4.6 Hypothesis testing introduction
4.7 Exploratory Data Analysis (EDA)
4.8 Finding trends and business insights
5.1 Importing Excel, CSV and SQL data
5.2 Power Query and data transformation
5.3 Relationships and data modelling
5.4 Star schema fundamentals
5.5 DAX measures and calculated columns
5.6 KPIs, charts, slicers and filters
5.7 Interactive dashboards
5.8 Publishing and presenting reports
6.1 End-to-end data analysis workflow
6.2 SQL + Python + Power BI integration
6.3 Data validation and report documentation
6.4 Business problem-solving and insight generation
6.5 Git and GitHub basics
6.6 Resume and portfolio preparation
6.7 Interview questions and mock interviews
6.8 Final project presentation
