Data Analyst Course Syllabus: Check Section Wise Topics List

Data Analyst Course Syllabus

Key Topics

Business Analytics Using Excel

• Data cleaning techniques, such as removing duplicates and handling missing values
• Descriptive statistics using built-in Excel functions
• Creating pivot tables to summarise data efficiently
• Data visualisation through charts, graphs, and dashboards
• Introduction to Excel macros and Power Query for automation and advanced tasks

SQL Fundamentals

• Basics of databases and understanding database management systems (DBMS)
• Writing SQL queries, including SELECT, WHERE, GROUP BY, and  ORDER BY
• Using JOINs (INNER, LEFT, RIGHT) to combine data from multiple tables
• Aggregating data with COUNT, SUM, AVG, MIN, and MAX
• Writing subqueries and nested queries
• Introduction to database normalisation and data modelling

Programming and Data Analytics with Python

• Python basics, including data types, loops, and  functions
• Data manipulation with the Pandas library (cleaning, transforming datasets)
• Numerical computations using NumPy and SciPy
• Statistical analysis and exploratory data analysis
• Data visualisation using Matplotlib and Seaborn
• Introduction to machine learning concepts and algorithms

Data Visualisation and Dashboarding

• Principles of effective data visualisation and storytelling with data
• Using tools like Tableau and Power BI for interactive dashboards
• Creating charts, maps, and reports for diverse data types
• Dashboard design best practices for business users

Fundamentals of Statistics and Probability

• Probability theory and distributions
• Hypothesis testing and confidence intervals
• Correlation and regression analysis
• Descriptive and inferential statistics
• Sampling techniques and data summarisation

Introduction to Machine Learning and AI

• Basic machine learning concepts and terminologies
• Supervised vs. unsupervised learning
• Common algorithms like linear regression, decision trees, and clustering
• Using Python libraries for building simple predictive models

Real-World Projects and Case Studies

• Data cleaning, exploration, visualisation, and reporting using real datasets.
• Industry case studies demonstrate how data analytics solves business problems across sectors like finance, marketing, healthcare, and technology.