Data Analytics Syllabus: Complete Course Outline

In an information-driven world, data evaluation and interpretation cannot be up to choice anymore; they are essentially prerequisite requirements. Data analytics transforms raw data into actionable knowledge, thus aiding businesses in foretelling trends, optimising operations, and enabling a smart decision.

Whether it’s understanding customer preferences, improving efficiency in the supply chain, or driving revenue growth, data analytics lies at the heart of everything.

The data analytics syllabus of the course offered by Hero Vired is designed to equip you with the skills needed to thrive in this rapidly growing field. A wide array of topics, starting from basic concepts like probability and statistics to advanced tools like Python, SQL, and Power BI, thereby linking theoretical knowledge with practical applications. Whether you are a beginner or upskilling for career progression, this course will serve as your gateway to mastering the art and science of data analytics.

If one wants to join this fast-emerging profession, knowing the data analytics syllabus forms the first step. This article outlines every requirement one needs to learn to become a data analyst.

Self-Paced: Comprehensive Introduction to Analytics and Its Applications

Before we start talking about tools and techniques, it’s important to understand the basics that we will discuss in the data analytics syllabus.

Here’s what this section covers:

What is Analytics?

Analytics is the science of retrieving meaningful insights from data. It helps find answers about things like customer trends or problems in operations.

Why Analytics Matters

Companies like Flipkart and Tata Consultancy Services rely on analytics to gain a competitive edge. It is not an option anymore-it is necessary.

Types of Analytics

• Descriptive: Summarising historical data to understand trends.
• Diagnostic: Identifying reasons behind past outcomes.
• Predictive: Forecasting future scenarios.
• Prescriptive: Recommending actions based on predictions.

Sources of Data

• Small Data: Simple spreadsheets.
• Big Data: Platforms like Hadoop manage terabytes of data.
• Traditional and Non-Traditional Sources: Think structured databases vs. social media streams.

CRISP-DM Framework

Managing analytics projects becomes simpler when following this structured approach.

Programming is the backbone of the data analytics syllabus. Here’s what you’ll cover:

Python Basics

• Variables, loops, and conditionals make coding simple and intuitive.
• Example: Use Python to calculate average sales for a retail store.

Functions

• Understand built-in and custom functions.
• Example: Write a function to automate missing value imputation.

Data Structures

• Work with lists, dictionaries, tuples, and sets.
• Example: Use dictionaries to group customer orders by region.

Also Read: Python Tutorial for Beginners

Mastering Complex Loops and Lambdas for Efficient Data Processing

Coding efficiently saves time and effort.

Nested Loops

• Use loops to process rows of a dataset effortlessly.

Lambda Functions

• Compact and effective for short operations.
• Example: Quickly filter sales above a threshold using a lambda function.

Combination of Loops and Lambdas

• Process large datasets in fewer lines of code.
• Example: Create a summary of product categories sold in different states.

File Handling for Data Organisation and Automation

Handling files is a core skill in analytics.

Basic File Operations

• Learn to read, write, and append data files.
• Focus on CSV files, which are widely used for storing data.

Practical Application

• Example: Automate daily sales report generation from CSV files using Python.

Mathematics Fundamentals for Data Analytics Applications

Math isn’t scary when broken down into simple concepts. Here’s what you’ll cover in data analytics syllabus:

Linear Algebra

• Understand vectors and matrices for data manipulation.
• Example: Use matrix operations to adjust pricing models.

Calculus

• Learn differentiation and integration for predictive modelling.

Statistics

• Get comfortable with mean, median, variance, and standard deviation.
• Example: Analyse customer satisfaction data using standard deviation to measure variability.

MS Excel Basics for Efficient Data Handling

Excel remains the go-to tool for data handling. In this module:

Basic Features

• Use shortcuts, filters, and data entry tools to organise data.
• Example: Apply filters to segment customers by region.

Summarising Data

• Functions like COUNTIF, SUMIF, and AVERAGE simplify calculations.
• Example: Identify the top-performing products in each category.

Visualisation

• Create basic charts and tables for quick insights.

Advanced MS Excel Techniques for Sophisticated Data Analysis

For more complex tasks, advanced Excel features step in.

Lookup Functions

• Master VLOOKUP, HLOOKUP, and MATCH-INDEX combinations.
• Example: Merge customer and order datasets using VLOOKUP.

Pivot Tables

• Create slicers and group data for dynamic analysis.
• Example: Build a pivot table to analyse sales by quarter.

Advanced Charting

• Use dynamic charts for presentations.
• Example: Show sales growth trends using combo charts.

Automation in Excel Using Macros and VBA

Repetitive tasks? Automate them with VBA.

Recording Macros

• Learn to automate processes step-by-step.

Custom Scripts

• Write VBA code to build interactive dashboards.

Practical Example

• Automate monthly sales reports with dynamic charts and data validation.

SQL Basics for Relational Data Management and Analysis

SQL is essential for working with databases. This data analytics syllabus includes the following:

• Set up and install SQL-related software on your local machine.
• Examine why SQL databases are a preferred choice for storing data.
• Explain the need for a client-server architecture in a database server.
• Appreciate why SQL is used as a query language to pull data from a database.
• Fetch data from one or some columns in a table.
• Use the WHERE clause to filter subsets of data.
• Use ORDER BY clause to sort data.
• Use GROUP BY clause with aggregations to summarise data.
• Use GROUP BY with HAVING Clause to filter grouped data.
• Use CASE statements to write conditional logic like IF ELSE statements.
• Work with DATE functions to perform date-related operations.
• Create Databases and Tables.
• Use INSERT command to add rows to the table.

Advanced SQL for Data Insights and Integration

Take your SQL skills further by covering these topics under the data analytics syllabus to get the most out of this course:

SQL Advance

• Use SQL Data Definition Language Statements to define the database structure or schema.
• Use Data Manipulation Language Statements to manage data within objects.
• Establish relationships in data to combine data from different tables.
• Analyse data after combining two or more tables using JOINS.
• Get insights on data using nested queries.

Relating data using SQL

• Establish relationships in data to combine data from different tables.
• Analyse data after combining two or more tables using JOINS.
• Get insights on data using nested queries.

SQL Analytics

• Implement Windows Analytic function in Data Science.
• Analyse the usage of the OVER clause with PARTITION FRAME and ORDER FRAME.
• Use Aggregate, Ranking, and Windows Analytical Functions for analysis and insights.

SQL and Excel

• Data manipulation over Excel from SQL.

Advanced Data Analytics Using Pandas and NumPy

Python libraries simplify data processing. These libraries make analytics faster and more efficient.

Pandas

• Import, clean, and manipulate datasets with ease.
• Example: Merge multiple CSV files into one clean dataset.

NumPy

• Perform advanced numerical operations on arrays.
• Example: Calculate correlations between sales and marketing spend.

Performing Descriptive Statistics with Python for Insightful Analysis

Statistics are the backbone of data analytics. Python’s statistical libraries make analysis accurate and reliable.

Central Tendency

• Calculate mean, median, and mode for quick insights.
• Example: Use mean to determine average sales per store.

Dispersion

• Understand variance and standard deviation.
• Example: Measure sales consistency across regions.

Comprehensive Understanding of Probability Distributions

Probability distributions help us predict outcomes based on historical data. They are essential for understanding patterns and making accurate forecasts.

Here’s what the data analytics syllabus covers:

Binomial Distribution

• This focuses on scenarios with two possible outcomes, like success or failure.
• Example: Calculate the probability of customers opting for a subscription service during a promotional campaign.

Poisson Distribution

• Use this distribution to measure how often an event happens over a period of time.
• Example: Predict how many orders a delivery service will receive in an hour.

Chi-Square Distribution

• Analyse the relationship between two variables.
• Example: Determine if there’s a significant association between customer demographics and product preferences.

Hypothesis Testing and Its Applications in Industry

Every business experiment starts with a question: Will this strategy work? Hypothesis testing gives us the tools to validate ideas using data. It is vital for data-backed decision-making.

Here’s how it works:

Define Hypotheses

• Null Hypothesis (H₀): Represents no change or effect.
• Alternative Hypothesis (H₁): Suggests a measurable change.
• Example: Test if a new pricing model increases sales.

Run Statistical Tests

• Choose the right test for your data:

1. T-Test: Compare means between two groups.
2. Chi-Square Test: Analyse relationships between variables.

Interpret Results

• A low p-value (e.g., <0.05) indicates significant results.
• Example: Use hypothesis testing to evaluate if a promotional discount increases customer purchases.

In-Depth Analysis with ANOVA and Statistical Testing Techniques

When we need to compare multiple groups, ANOVA (Analysis of Variance) becomes invaluable. These statistical tools provide clarity and confidence in our analysis.

Here’s what this section covers:

One-Way ANOVA

• Compare the means of three or more groups.
• Example: Evaluate the performance of different marketing channels (e.g., email, social media, and in-store campaigns).

Two-Way ANOVA

• Examine how two factors interact.
• Example: Analyse how the region and advertising type impact sales figures.

Chi-Square and Non-Parametric Tests

• Use these when data doesn’t follow a normal distribution.
• Example: Check if customer satisfaction ratings differ by age group.

Basics of Probability and Frequency Distributions for Analytics

Probability helps quantify uncertainty, while frequency distributions visualise how data is spread. Covering these fundamentals in the data analytics syllabus sets the stage for more advanced analytics.

Here’s what we’ll cover:

Probability Basics

• Learn about mutually exclusive events (e.g., heads or tails) and independent events (e.g., rolling two dice).
• Example: Estimate the likelihood of a customer making a repeat purchase.

Frequency Distributions

• Discrete: Fixed outcomes, like the number of orders per day.
• Continuous: Data that varies, like customer transaction amounts.
• Example: Observe transaction patterns to identify peak purchasing times.

Introduction to Power BI for Data Visualisation and Reporting

Visuals make data actionable, and Power BI excels in this area.

Here’s what you’ll learn:

Filtering Data

• Narrow down datasets to focus on specific insights.
• Example: Analyse sales performance in major Indian cities like Mumbai and Delhi.

Creating Visuals

• Choose the best chart types for your data.
• Example: Use a line chart to track monthly revenue trends.

Joining Data

• Combine datasets for a complete analysis.
• Example: Merge customer profiles with purchase data to identify trends.

Advanced Data Slicing and Dicing Using DAX Query in Power BI

DAX (Data Analysis Expressions) adds a layer of sophistication to Power BI.

Here’s what you’ll explore:

Custom Measures

• Create dynamic calculations like year-over-year growth rates.
• Example: Calculate profit margins for different product categories.

Advanced Visuals

• Use treemaps, scatter plots, and funnel charts for deeper analysis.
• Example: Visualise drop-offs in a customer onboarding process with a funnel chart.

Dashboarding and Storytelling with Power BI for Effective Communication

Dashboards simplify complex data, and storytelling makes insights actionable.

This section of the data analytics syllabus includes:

Building Dashboards

• Combine visuals into interactive, user-friendly dashboards.
• Example: Create a sales performance dashboard highlighting targets vs achievements.

Data Storytelling

• Use the ABT (And, But, Therefore) framework to narrate data.
• Example: “Sales are rising (And), but customer retention is falling (But), so we need targeted loyalty programmes (Therefore).”

Professional Formatting

• Add custom branding and consistent colours for clarity.

Comprehensive End-to-End Data Analytics Project

Learning by doing is the best way to master analytics. This project brings together all your skills and prepares you for real-world analytics challenges.

Here’s the process:

Data Cleaning

• Remove duplicates, handle missing values, and standardise formats.
• Example: Clean customer feedback data for analysis.

Data Analysis

• Use Python, SQL, and Excel to extract insights.
• Example: Segment customers based on buying patterns.

Data Visualisation

• Build a Power BI dashboard to present your findings.
• Example: Showcase sales trends and recommendations for improvement.

Applying Business Analytics Methods for Strategic Insights

Strategic decisions require structured frameworks. This section covers:

SWOT Analysis

• Identify strengths, weaknesses, opportunities, and threats.
• Example: Evaluate launching a new product line.

PESTLE Framework

• Analyse external factors like political and economic influences.
• Example: Study how GST reforms impact pricing strategies.

Value Chain Analysis

• Optimise operations by understanding value-adding activities.
• Example: Streamline supply chain processes to reduce costs.

Forecasting Techniques for Predictive and Prescriptive Analytics

Forecasting techniques help businesses stay ahead of the curve.

Here’s what you’ll learn in the data analytics syllabus:

Predictive Models

• Use historical data to forecast future trends.
• Example: Predict demand for air conditioners during summer.

Optimisation Techniques

• Determine the best actions to maximise outcomes.
• Example: Allocate advertising budgets across digital platforms.

Sensitivity Analysis

• Assess how variables impact outcomes.
• Example: Analyse how price changes affect sales volumes.

Data-Driven Decisions for Business Optimisation

Data is at the core of every successful business decision today. Using analytics, we can turn raw numbers into actionable strategies and make businesses more agile and efficient.

Here’s what we’ll focus on:

Business Process Improvement

• Identify inefficiencies and streamline operations.
• Example: Analyse warehouse operations to reduce delivery delays.

Customer Insights

• Understand what drives customer loyalty.
• Example: Use transaction data to determine which offers bring repeat purchases.

Performance Metrics

• Set and track KPIs (Key Performance Indicators) for better accountability.
• Example: Monitor monthly sales growth compared to targets.

Also Read: Career in Data Analytics in India

Leveraging GenAI Data Analysis for Modern Analytics Applications

Generative AI is reshaping the way we work with data. Tools like ChatGPT and Gemini are making analytics faster and smarter. Generative AI makes analytics accessible to everyone, regardless of technical expertise.

Here’s how they’re used:

ChatGPT for Data Tasks

• Automate repetitive coding tasks like data cleaning or formatting.
• Example: Generate Python scripts to handle missing data in customer records.

Big Data Analysis with Gemini

• Quickly process large datasets for insights.
• Example: Analyse millions of transactions to identify spending trends.

Agile Project Management Techniques in Data Analytics

Managing analytics projects effectively requires flexibility and collaboration. Agile methodologies help ensure projects are delivered on time and meet goals.

Key aspects include:

Agile Frameworks

• Break projects into smaller, manageable sprints.
• Example: Deliver weekly insights on customer engagement metrics.

Collaborative Tools

• Use platforms like Jira or Trello to track progress and allocate tasks.
• Example: Assign data cleaning to one team and dashboard creation to another.

Best Practices for Adaptability

• Adjust plans based on feedback or changing requirements.
• Example: Shift focus to regional sales analysis based on new client priorities.

Also Read: Top 80 Data Analytics Interview Questions with Answers

Capstone Project: Integrating Learning into Practical Applications

The capstone project is the ultimate test of your analytics skills. It’s where everything you’ve learned comes together in a real-world scenario.

Here’s what it includes:

Define Objectives

• Start with a clear goal, like improving customer retention or optimising inventory management.

Apply Tools and Techniques

• Use Python, SQL, Power BI, and Excel to analyse data and draw conclusions.
• Example: Create a Power BI dashboard showcasing key insights.

Present Findings

• Communicate your results effectively to stakeholders.
• Example: Recommend actionable strategies to improve revenue growth.

Conclusion

Data analytics is more than just a skill; it is the foundation of modern decision-making. This detailed data analytics syllabus brings out the most important tools, techniques, and methodologies necessary to master this domain.

From basic concepts like probability distributions and hypothesis testing to the advanced tools of Python, SQL, Power BI, and generative AI, each module positions you to solve real-world challenges.

Practising building dashboards, improving business processes, or developing decision-making based on data are skills that help craft a successful career in analytics.

The data analytics syllabus integrates analytical skills with real-life applications, forming a strong foundation for success in a world focused on data. Begin your analytics journey today!

Hero Vired’s Certification Program in Data Analytics would be the best option if you are advancing to the next level, as it has a curriculum specifically designed to bridge theoretical knowledge with practical application and industry-relevant projects, along with mentorship with experts. The program will keep you geared toward building the expertise of a competent data analyst.