Data Analytics Course Syllabus

Introduction to Data Analytics

The Data Analytics course syllabus introduces learners to the concepts, tools and techniques used to understand data and support business decisions. It covers the role of data analytics across different industries and explains how organizations use information to identify patterns, solve problems and improve performance. AtFITA Academy, learners explore these concepts through a structured program that combines technical knowledge with practical exercises.

• Introduction to Data Analytics and its importance.
• Difference between Data Analytics, Data Science and Artificial Intelligence.
• Applications of Data Analytics across industries.
• Career opportunities and future scope of data analytics.
• Popular tools used in data analytics and business intelligence.
• Importance of SQL, Python and statistics in data analysis.

Data Analytics in the Age of AI

The Data Analytics course syllabus also introduces the role of artificial intelligence in modern analytical work. Learners understand how AI tools support data interpretation, query writing and reporting while learning why AI-generated results must be reviewed and validated.

• Changing role of data analysts with AI adoption.
• Understanding the AI co-pilot approach to data analysis.
• Introduction to ChatGPT, Microsoft Copilot, Gemini and Claude.
• Using AI to generate analysis plans and explore datasets.
• Understanding AI limitations, hallucinations and inaccurate outputs.
• Importance of SQL and Python when working with AI tools.

SQL Fundamentals and Database Management

Learn relational database concepts and the fundamentals of SQL. This part of the Data Analyst course syllabus helps learners understand how databases are created, managed and accessed using commonly used database systems.

• Introduction to SQL and relational database concepts.
• Working with SQL Server, MySQL and Oracle Database.
• Creating, modifying and managing databases.
• Creating database users and managing access.
• Creating, updating and deleting tables.
• Understanding SQL data types, keywords, constraints and operators.

SQL Queries and Data Retrieval

Develop the ability to retrieve, filter, and organize information through SQL queries. Learners practice using different commands and conditions to extract relevant information from databases. This topic is an important part of the Data Analytics syllabus, helping learners build practical skills in database management and data retrieval.

• Writing SELECT queries and using DISTINCT and TOP.
• Applying WHERE, BETWEEN, IN and LIKE conditions.
• Using AND, OR, NOT and CASE statements.
• Sorting and grouping data using ORDER BY and GROUP BY.
• Working with COUNT, AVG, SUM, MIN and MAX functions.
• Using HAVING, EXISTS, ANY and ALL conditions.

Advanced SQL and Database Operations

Explore advanced SQL concepts used to work with related datasets and perform database operations. The Data Analyst syllabus also covers functions and database features that support efficient data retrieval and management.

• Working with INNER, LEFT, RIGHT, FULL, SELF and CROSS JOINs.
• Understanding UNION and advanced joins.
• Creating and managing SQL views.
• Working with stored procedures and user-defined functions.
• Exploring string, date and mathematical functions.
• Understanding indexes, query execution, backup and restoration.

AI-Assisted SQL and NL2SQL

Learn how AI tools can convert business questions written in natural language into SQL queries. Understand how to generate, improve and validate queries based on database requirements.

• Introduction to Natural Language to SQL (NL2SQL).
• Using AI tools for SQL query generation.
• Converting business questions into SQL statements.
• Writing effective prompts with database schema context.
• Using AI for SQL optimization and performance improvement.
• Validating AI-generated queries and correcting errors.

Power BI Fundamentals and Data Connectivity

The Data Analytics course syllabus covers Microsoft Power BI and its role in business intelligence. This section explains how to connect different data sources and prepare datasets for reporting.

• Introduction to Power BI Desktop and Power BI Service.
• Understanding Power BI Free and Pro versions.
• Introduction to the ETL process.
• Connecting to databases, Excel files, CSV files and other data sources.
• Understanding data types, fields and data models.
• Exploring the Power BI workspace and report designer.

Power BI Reports and Data Visualization

Learners can follow the Data Analyst course syllabus for beginners to understand how Power BI helps create clear, interactive reports and present data effectively through charts and visualizations.

• Creating reports with tables, charts and multiple visualizations.
• Working with bar, column, line, area, pie, scatter and waterfall charts.
• Applying visual-level, page-level and report-level filters.
• Using conditional formatting and customizing report properties.
• Creating maps, hierarchies and drill-down reports.
• Publishing, exporting and sharing Power BI reports.

Power Query, Data Modeling and DAX

Understanding how to transform raw datasets and build relationships between tables. Learners work with Power Query and DAX to prepare data and perform calculations for analytical reports.

• Extracting and transforming data using Power Query Editor.
• Cleaning datasets by removing empty rows, splitting columns, and handling missing values.
• Working with merge, append, pivot, and transpose operations.
• Creating and managing relationships between tables.
• Understanding DAX, calculated columns, and measures.
• Using slicers and interactive filtering options.

Power BI Copilot and AI-Assisted Reporting

Explore AI features in Power BI that can simplify report creation and support business analysis. Learn to review AI-generated results and make corrections when required.

• Introduction to Power BI Copilot and its capabilities.
• Generating DAX measures using natural language.
• Creating report visuals using plain English prompts.
• Generating automated summaries and business narratives.
• Validating AI-generated reports, calculations and insights.

Python Programming Fundamentals

The Data Analytics course syllabus at FITA Academy includes Python programming fundamentals to help learners work with data and automate common analytical tasks. Learners build their programming knowledge through basic concepts and practical exercises.

• Introduction to Python, its features and applications.
• Setting up Python and configuring the coding environment.
• Understanding variables, identifiers, keywords and data types.
• Working with lists, tuples, dictionaries, sets and ranges.
• Using conditional statements, loops and functions.
• Exploring Python modules, packages and file handling.

Python Libraries for Data Analytics

Get introduced to Python libraries that help analysts manipulate, process and visualize data. Understand how these libraries simplify common analytical tasks.

• Introduction to NumPy for numerical operations.
• Working with Pandas for data manipulation.
• Using SciPy and Statsmodels for statistical analysis.
• Exploring Matplotlib and Seaborn for data visualization.
• Introduction to Scikit-learn for data analysis applications.
• Understanding debugging, breakpoints, and IDE tools.

Data Manipulation and Preprocessing Using Python

Learn how to prepare raw datasets for analysis using Python. The Data Analyst course syllabus covers the steps involved in collecting, cleaning, transforming, and validating data before it is used for reporting.

• Understanding data acquisition and extraction.
• Inspecting and managing raw and processed datasets.
• Cleaning data and handling missing values.
• Transforming and integrating datasets.
• Performing data validation and quality checks.
• Working with DataFrames, indexing, filtering, aggregation, and reshaping.

Statistics and Exploratory Data Analysis

Understanding the statistical concepts required to interpret datasets and identify meaningful patterns. The Data Analytics subject syllabus includes descriptive statistics, probability, and exploratory techniques used to examine data.

• Understanding descriptive and inferential statistics.
• Working with mean, median, mode, variance, and standard deviation.
• Exploring probability, distributions, and conditional probability.
• Understanding hypothesis testing, p-values, T-tests, chi-square, and ANOVA.
• Performing univariate and bivariate analysis.
• Identifying outliers, trends, missing values, and relationships in datasets.

Prompt Engineering for Data Analysts

Learners can understand how Power BI helps create clear, interactive reports and present data effectively through charts and visualizations. This topic is an important part of the Introduction to Business Analytics syllabus and helps learners develop skills in data presentation and interpretation.

• Introduction to prompt engineering and its importance.
• Understanding the RCTF framework (Role, Context, Task and Format).
• Generating Python and Pandas code using AI prompts.
• Creating prompts for data cleaning and exploratory analysis.
• Refining AI-generated insights and business narratives.
• Identifying prompting errors and validating AI outputs.

Data Visualization Using Tableau

Learn to explore datasets and create interactive visualizations using Tableau. This part of the syllabus of Data Analytics introduces business intelligence concepts, data connections and reporting techniques.

• Introduction to Business Intelligence and data visualization.
• Understanding Tableau architecture, workspace and data sources.
• Connecting to live and extracted data sources.
• Working with dimensions, measures, joins, unions and data blending.
• Creating charts, graphs, maps and statistical visualizations.
• Applying filters, parameters, calculated fields and hierarchies.

Advanced Tableau and Dashboard Development

Explore advanced Tableau features for building interactive dashboards and presenting complex datasets as part of Data Analyst course syllabus. Learners also understand how to publish and manage visualizations through Tableau platforms.

• Creating interactive dashboards and advanced reports.
• Working with forecasting, trend lines, and statistical calculations.
• Exploring drill-downs, sets and data hierarchies.
• Creating maps and geographical visualizations.
• Understanding Tableau Public and publishing dashboards.
• Introduction to Tableau Server, user management and data security.

AI-Enhanced Capstone Projects

Apply the concepts covered in SQL, Python and Power BI through practical projects. Learners work with datasets to prepare, analyze, visualize, and forecast data while using AI tools to support insight generation and reporting.

Projects include:

• AI-Enhanced Retail Sales Analysis and Forecasting.
• COVID-19 Data Analysis, Visualization and AI-Enhanced Reporting.

AI-Assisted Coding

Explore AI-assisted application development using generative AI tools and coding agents. Understand AI and GenAI fundamentals, large language models, context windows, prompt engineering, and the limitations of AI-generated code. Practice using tools such as Google Antigravity, OpenAI Codex, and GitHub Copilot for application development, testing, debugging, code review, and deployment through Vercel. The Syllabus of Data Analytics course also covers how these AI tools can improve coding workflows, streamline development tasks, and support efficient application building.

Additional Career Development Courses

• Advanced Excel
• Aptitude Training
• Spoken English and Soft Skills

Career Opportunities After the Course

The Data Analytics full syllabus covers skills that can help learners prepare for roles such as Data Analyst, Business Analyst, Reporting Analyst and Junior Data Analyst. Career opportunities and salary levels may vary based on skills, experience, location, and the organisation.

The Data Analytics certification component can help learners demonstrate their understanding of key tools and concepts. Practical project experience and the ability to explain analytical decisions are also useful when preparing for job interviews.

Learners can prepare for technical discussions by referring to Data Analytics Interview Questions and Answers and practicing questions related to SQL, Python, statistics, and visualization.

Overall, the Data Analytics course syllabus combines database management, programming, statistics, business intelligence, visualization, and AI-assisted analysis. It is structured to help learners build skills progressively, from understanding datasets to presenting insights through reports and dashboards.