Data Analyst Course Syllabus

A data analyst collects, cleans, and analyzes data to help businesses make informed decisions. They handle data issues such as missing values and outliers and gather information from sources like surveys or web monitoring. Analysts present their findings through visual tools like graphs and reports. They work across various industries, including healthcare and retail, and typically have a mathematics, statistics, or computer science degree. If you are interested in building a career in this field and would like to explore a professional course, read our guide on data analyst course syllabus. It will offer tips to get started as a data analyst.

Who is a Data Analyst? An Overview

A data analyst deciphers data to uncover important insights about a company’s clientele and ways the data can be utilized to address issues. The role also involves conveying the analytical information to the company’s leadership and other stakeholders. A data analyst methodically gathers, processes, and conducts statistical assessments of data sets. Some of the responsibilities of a data analyst involve data cleansing and data preparation. In simple terms, their job entails sifting through the data, addressing missing values, and preparing the dataset for analysis to ensure precision and pertinence.

If you are interested in a career in data analysis, enroll in a data analyst course with a placement guarantee. Studying the data analyst syllabus will help you gain important technical skills and secure a job after completion.

Data Analyst Course Syllabus: Core Components

The components of a data analyst syllabus typically encompass various subjects aimed at providing students with the knowledge and skills to analyze and interpret large datasets. Here’s an outline of the essential elements:

• Data Analytics: Data analytics is a process of transforming raw data into valuable information. This segment will cover various statistical methods and predictive modeling techniques to identify trends and patterns in the data, enabling informed, data-driven decision-making. 

• Data Management: SQL is one of the primary tools used to manage and organize databases. This module will teach students how to handle large datasets (big data) and data governance, security, and integrity. This knowledge will enable efficient storage and retrieval of data, preparing it for further analysis.

• Data Visualization: Data visualization involves presenting complex information in a simple, visual format. Students will learn to create charts, graphs, and dashboards using tools like Tableau and Python libraries such as Matplotlib. 

• Data Analysis: This segment focuses on using advanced statistical methods like regression analysis, clustering, and hypothesis testing to analyze data. Such techniques help uncover hidden trends and relationships within the data.

• Data Preparation & Analysis: This area of data analysis teaches how to clean, organize, and structure raw data for optimal results. This includes handling missing data, engineering new features, and detecting outliers to ensure that datasets are prepared for high-quality analysis.

• Machine Learning: Machine learning involves teaching computers to learn from data and make predictions. This module covers different algorithms such as decision trees, random forests, and support vector machines. Students will learn how to build, train, and evaluate models capable of solving real-world problems, such as forecasting or creating recommendation systems.

• Natural Language Processing: Natural language processing focuses on how computers can understand and interact with human language. Students will learn techniques for processing text and speech, including tasks like sentiment analysis and language translation. 

• Deep Learning: Deep learning is a branch of machine learning that concentrates on neural networks and artificial intelligence. You learn advanced models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), used for complex tasks such as image recognition and natural language understanding.

Introduction to Data Analytics

Check the data analyst course syllabus before enrolling. The syllabus should include an overview of data analytics and its role in making informed business decisions. This section will equip you with the essential skills necessary for a successful career in data analysis. Also, consider exploring the highest-paying data analyst jobs in India.  

Here are the elements you will learn in this course module:

1. Introduction to Analytics

This section aims to provide a foundational understanding of data analytics and its role in facilitating intelligent business decisions. Participants will gain insights into three primary types of analytics: descriptive, predictive, and prescriptive analysis. These are essential for shaping contemporary business strategies. They use historical data to predict future outcomes and recommend actions.

2. Extracting Data Using Industry Standards

This segment will cover techniques for proficiently gathering and extracting data. Participants will learn to utilize tools like SQL and APIs to ensure accurate and dependable data collection, a crucial step preceding any analysis.

Data Management and Analysis with MS Excel

In this segment, students will focus on mastering Excel, a powerful tool for data management and analysis. The following topics will be explored in this segment of the data analyst syllabus:

1. Getting Started with Excel

Excel is a powerful spreadsheet application that serves as an essential tool for data management, analysis, and visualization. This segment is designed for beginners seeking to grasp the fundamentals of Excel. In this section, you will learn: 

• Organizing, editing, and formatting data.
• Use of basic tools for managing datasets.
• Understanding of Excel.

2. All About Functions and Formulas

This subsection delves into the advanced tools available in Excel, focusing on essential formulas and functions that improve data analysis. The key topics include:

• You will explore the list of basic and advanced Excel formulas and functions, such as the  VLOOKUP formula, INDEX-MATCH, and IF statements.
• You will learn how Excel functions are necessary for intricate calculations and data manipulation, enhancing work efficiency and accuracy.

3. Analyse and Summarise Data

Participants will learn to create concise summaries of extensive datasets using Pivot tables and charts. These skills will enable you to learn to transform raw data into actionable insights and facilitate easier comprehension of trends and patterns.

4. Explore More Features in Excel

This module introduces participants to advanced Excel features like data validation, what-if analysis, and conditional formatting, enabling a deeper understanding of data and informed decision-making.

5. Automate with Excel

Participants will acquire knowledge on automating repetitive tasks in Excel, including recording macros, and writing VBA scripts, unlocking Excel’s automation capabilities and streamlining workflow processes.

Pro Tip: To gain a comprehensive knowledge of Excel, consider taking a specialized advanced Excel course that covers all essential skills and techniques.

Data Visualization

Data visualization is an essential skill for any data analyst. In this section, students will learn the principles of visual storytelling and how to create impactful visual representations of complex datasets. Here are the topics that will be covered in this module of the course: 

1. Understanding the Basics of Power BI

This section will cover the foundational aspects of Power BI, including its interface and functionalities. This is what you will learn:

• You will delve into constructing dashboards and reports that effectively convey data insights.
• The knowledge you will gain from this section will lay the groundwork for understanding Power BI’s role in visualizing data.

2. Data Preparation and Data Model Fundamentals

You will be guided through the process of importing data into Power BI, cleaning it, and organizing it for analysis. The objective is to establish a cohesive data model by linking various datasets, ensuring that the data is well-structured and prepared for in-depth analysis.

3. Manipulate Data, Create Models, and Visualization

You will learn how to transform data into meaningful visual narratives using Power BI in this module. You will see how raw data can be transformed into valuable insights through visual elements such as charts, graphs, and maps.

4. Intermediate Data Transformation and Visualization

This data analyst course syllabus module will take your skills to the next level by delving into more intricate data manipulation. You will learn to create interactive reports that enable users to dynamically explore data, simplifying the extraction of insights from extensive datasets for businesses.

5. Advanced-Data Transformation and Visualization

This advanced module equips you to handle more intricate data scenarios. You will immerse yourself in advanced transformation techniques, time-series analysis, and real-time data visualization, enabling the delivery of impactful insights that can influence strategic decisions.

6. All About Dax

Data Analysis Expressions (DAX) is a formula language within Power BI. This segment will provide insights into using:

• DAX to create customized metrics
• Key Performance Indicators (KPIs) 
• Advanced calculations

7. Working on The Cloud

This module will cover how to utilize Power BI’s cloud capabilities to distribute reports, collaborate with others, and provide real-time data updates. You will gain an understanding of how cloud-based Power BI can enhance teamwork and enable real-time decision-making across your organization.

Pro Tip: To enhance your skills in data visualization, consider taking a dedicated Power BI course that will provide you with hands-on experience and practical examples.

Data Analysis 

In this section of the data analyst syllabus, students will dive deeper into the analytical techniques used to extract meaningful insights from data. Aside from learning the technical skills, you should also prepare an effective job application, including a data analyst cover letter, resume, portfolio, etc. It will help you find the right job opportunity in the data analysis field.

Here are the topics that will be covered in this course module: 

1. Introduction to Database and SQL

Databases are utilized for storing, organizing, and managing large volumes of data. This section serves as an introduction to databases and their role in data storage. You will learn about the structured query language (SQL), a vital tool as it facilitates interaction between data sets. It enables retrieval, updating, and manipulation of data stored in relational databases.

1. Introduction to SQL

Once familiar with databases, you will learn structured query language (SQL) by composing simple queries. The SELECT statement is among the initial concepts to be grasped, aiding in the retrieval of specific data from a database. Additionally, you will utilize the WHERE clause to filter data, displaying only the pertinent information for your query. Furthermore, sorting data results will be covered to enhance readability.

2. Intermediate SQL

After mastering the fundamentals, you will progress to intermediate SQL concepts that will expand your querying capabilities. You will explore more complex statements and functions, such as:

• JOIN operations for combining data from multiple tables. 
• GROUP BY clause to summarize your data, a valuable technique for analysis. 
• Lastly, the HAVING clause will be introduced to facilitate the filtering of summarized data, offering additional control over your queries.

3. Advanced SQL

This section will elevate your SQL proficiency to a more advanced level. In this section, we will explore advanced SQL techniques that enable you to perform complex data analysis and manipulation. Here is what you will learn:

• You will learn to construct complex queries using subqueries, enabling the execution of multiple operations within a single query. 
• Window functions will empower you to conduct detailed data analysis by processing rows associated with the current row.

Pro Tip: To master advanced SQL techniques, taking a specialized SQL course can help you practice complex queries and deepen your analytical skills.

Data Preparation & Analysis

Data preparation and analysis will be the focus of this section of the data analyst course syllabus. An effective data preparation process ensures that the data is clean, relevant, and ready for meaningful exploration in a data analysis project. The following topics will be addressed in this module of the course: 

1. Introduction to Python

Python’s simplicity and readability make it an ideal choice for beginners due to its versatility and extensive applicability. This section will cover the fundamental syntax of Python and explain why it is widely used in data analytics. 

2. Flow Control

In this section, we will delve into flow control in Python, which allows you to manage the execution of your code based on certain conditions. Here’s what you will learn:

• You will learn how to manage code flow using loops, conditionals, and other control structures. 
• You will also explore tools that will enable you to handle data more effectively by allowing your programs to make decisions and repeat actions. These are essential concepts in data processing.

3. Data Structures and Functions

In this section, you will explore fundamental data structures in Python. Understanding these structures is essential for organizing and managing data efficiently, as each type offers unique benefits for specific tasks. Here is what you will explore:

• You will learn about Python’s pre-built data structures such as lists, dictionaries, and tuples. These structures are crucial for efficiently organizing and manipulating data. 
• You will also learn how to create reusable functions, which will enhance the code’s cleanliness and manageability, especially when working on larger projects.

4. Error and File Handling

This section of the syllabus of the data analyst course will teach you how to manage errors and handle files in Python, both of which are important skills for any data analyst. This knowledge will help you write more robust code that can handle unexpected issues. In this module: 

• You will learn how to gracefully handle exceptions and errors in your code. 
• Additionally, you will practice reading from and writing to files, which is vital for loading datasets and saving results.

5. Python Libraries

Python libraries such as NumPy, Pandas, and Matplotlib are indispensable for data analysis. Here, you will learn how to use these libraries for numerical data manipulation, statistical analysis, and data visualization, assisting you in deriving insights from your datasets.

6. Introduction to Statistics

This section will introduce key statistical concepts such as mean, median, and mode, as well as measures of spread-like variance. These concepts are essential for summarizing and interpreting data.

7. Methods to Find Centralized Values

Here, you will explore measures of central tendency and dispersion, which are essential tools for summarizing your data. They provide quick insights into the general patterns of your dataset.

8. Inferential Statistics

Finally, you will learn about inferential statistics, which helps in making predictions based on sample data. You will explore hypothesis testing and confidence intervals, which are essential for making data-driven decisions.

Pro Tip: To deepen your understanding of data analysis and Python’s role in it, consider taking a comprehensive Python course that covers all the essential techniques and libraries.

Machine Learning

This module of the data analyst course syllabus covers the fundamentals of machine learning. Understanding machine learning techniques equips data analysts with the tools to uncover patterns and make data-driven predictions. The following topics will be covered in this module of the course: 

1. Introduction to Machine Learning

In this section, you will explore the fundamental concepts of machine learning. Machine learning enables computers to learn from data without being explicitly programmed. These models can identify patterns and make predictions based on historical data, which is useful in various fields like finance, healthcare, and marketing.

2. Supervised Machine Learning

This subsection focuses on how supervised machine learning works with analysis. Supervised learning is a common machine learning technique where models are trained on labeled data. This means the algorithm learns from input-output pairs and can predict outcomes for new data. You’ll learn popular algorithms like linear regression for predicting continuous values, decision trees for classification, and support vector machines.

3. Unsupervised Machine Learning

In contrast to supervised learning, unsupervised learning deals with unlabeled data. The goal here is to discover hidden patterns and structures in the data. You will learn techniques like clustering, where algorithms such as K-Means group similar data points together.

Natural Language Processing

Natural language processing (NLP) is a specialized field of artificial intelligence that focuses on the interaction between computers and human language. In this module of the course, the following topics will be covered: 

1. Introduction to Natural Language Processing

The study of natural language processing (NLP) is crucial in the field of data science as it involves the comprehension and analysis of human language by computers. This section will provide a foundational understanding of NLP, emphasizing its significance in text analysis and interpretation. 

2. Understanding of Natural Language Processing

Following the foundational knowledge, you will explore specific techniques utilized in NLP. This section will include an examination of tokenization, which involves breaking down text into smaller components, and sentiment analysis, which identifies the underlying emotion in the text. 

3. Advanced NLP

Once a strong foundation is established, advanced NLP topics will be introduced. This section will focus on intricate NLP models, particularly transformers, which are powerful tools capable of handling complex tasks like text generation and question-answering. 

Pro Tip: To deepen your understanding of NLP techniques and their applications, consider enrolling in a comprehensive natural language processing course that covers both foundational and advanced topics.

Deep Learning Mastery: Data-Intensive

In this advanced section of the data analyst syllabus, students will master deep learning techniques, a subset of machine learning. Deep learning is effective for image and speech recognition, natural language processing, and complex predictive modeling. In this module of the course, the following topics will be covered: 

1. Introduction to Deep Learning

Deep learning, a branch of artificial intelligence that centers on neural networks, will be explored in this section. An overview of deep learning and its functionalities will be provided, along with an examination of various applications such as speech recognition and natural language processing, highlighting its impact across different industries. 

2. From Neural Networks to CNNs

Building on the foundational understanding, a deeper exploration of neural networks and Convolutional Neural Networks (CNNs) will be conducted. CNNs are specialized for processing grid-like data, such as images. The focus will be on understanding the operations and unique layers of these networks.

Exciting Data Analytics Projects

As part of this comprehensive syllabus, students will engage in hands-on projects that cover the concepts and techniques learned. These projects are designed to provide practical experience, allowing students to apply and showcase their skills to potential employers. 

Here are the projects that you will have to complete in this syllabus:

1. Legal Documents Categorized by J P Morgan

Building on the foundational analytics concepts learned in this course, you will work on classifying legal documents using JP Morgan’s data analytics framework. This project will demonstrate how data analytics can revolutionize industries by making processes faster, more accurate, and more efficient.

2. Excel Data Analysis Conducted by KPMG

In this comprehensive Excel project, you will apply skills learned from five modules. The project involves collecting and cleaning data, performing detailed analysis, creating visualizations, and generating actionable insights. You will use Excel’s advanced features including functions, formulas, and data summarization tools for this project. By the end, you will have a better understanding of how to leverage Excel for meaningful data analysis that can inform business strategies.

3. Power BI Used for Analyzing Airline Performance

In this visualization project, you will create an airline performance dashboard using Power BI. The project involves creating data models and implementing DAX formulas to analyze airline performance metrics. This project will help you understand how data visualization tools can enhance operational efficiency and improve airline performance.

4. Walmart Case Study for Retail Insights Optimization

This project involves analyzing Walmart’s operations using advanced SQL techniques. You will use various SQL concepts including database fundamentals, intermediate querying techniques, and advanced SQL operations. The analysis aims to enhance strategic decision-making and optimize business processes, ultimately improving operational efficiency.

5. Electric Vehicles Market Analysis Using Python Project 

This project aims to analyze the market size of electric vehicles using Python. You will apply Python fundamentals, data structures, error handling, and statistical analysis techniques. This project will provide valuable experience in data handling and market analysis, showcasing how Python can be used to draw meaningful insights.

6. Smart Pricing & Retention Analysis Project 

This advanced machine learning project combines two components showcasing regression and classification techniques. This project consists of:

• Airbnb Price Prediction: You will learn to use regression models to accurately estimate listing prices using data preparation and feature engineering techniques.
• Telecom Customer Churn Prediction: You will build classification models to predict customer churn by applying machine learning classification concepts.

7. Text Intelligence Project 

This natural language processing (NLP) project consists of two practical applications:

• IMDb Review Sentiment Analysis: You will apply text preprocessing and classification techniques to analyze movie review sentiments.
• News Article Classification: You will develop models to categorize news articles into different topics using advanced NLP concepts.

How to Get Started as a Data Analyst?

With the rise in the importance of data in today’s world, becoming a data analyst can be a rewarding career path. From understanding the necessary skills to gaining practical experience, we will cover everything you need to know to begin your career in this field.

• Develop Essential Skills: Equipping yourself with the right skills is necessary for success as a data analyst. Enroll in online courses and certifications that cover the fundamental aspects of the curriculum and a comprehensive data analyst course syllabus. Opt for well-established institutions or online platforms that provide extensive training programs.

• Gain Practical Experience: Acquire practical experience by engaging in real-world projects, taking part in internships, or contributing to open-source projects. A comprehensive data analyst curriculum should include industry projects to facilitate hands-on learning.

• Networking: Establish a professional network within the data analytics community by attending industry events, participating in online forums, and connecting with professionals on platforms like LinkedIn.

• Build a Strong Portfolio: As you gain experience, it’s essential to build a portfolio showcasing your projects. This portfolio will demonstrate your technical abilities and also your problem-solving skills and creativity to potential employers. Consider using Git and GitHub to manage your projects and collaborate with others. These platforms allow you to showcase your code and track your progress, making it easier for recruiters to view your work and understand your development process. 

Conclusion

After completing the data analyst course syllabus successfully, you will possess a solid grasp of essential concepts, tools, and techniques necessary for effectively analyzing data. The courses provide hands-on experience in data manipulation, statistical analysis, machine learning, and data visualization, enabling you to extract valuable insights from intricate datasets. At the end of the syllabus of data analyst, you will also have a compelling portfolio of projects to showcase your skills. 

If you are interested in learning more about career prospects in this field, explore our blog on data science career opportunities.

FAQs