Data Analyst Course Syllabus: Check Section Wise Topics List

The syllabus for data analyst comprises topics like Business Analytics Using Excel, SQL Fundamentals, Programming and Data Analytics with Python and more. Check the detailed data analyst course syllabus in the article.

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Data Analyst Course Syllabus: Section Wise Topics List

Data analyst courses are high in demand in the modern world because organisations are increasingly depending on data-orientated solutions to make decisions. A properly designed data analyst course syllabus gives the learner the practical and theoretical understanding required to succeed in this ever-changing career. The data analyst course syllabus typically covers core skills such as Python for data analysis , SQL, data visualization tools like Tableau and Power BI, and so on. If you are interested in making a career in this field, you should be well-versed with all the key topics. This article is an overview of a detailed data analyst syllabus, including fundamental modules that equip aspirants to gather, clean, analyse and visualise data effectively.
Also Check - How to Become a Data Analyst?

Data Analyst Course Syllabus: Section Wise Topics List

Check the data analyst course syllabus along with their key features in the table below:

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.

A full-course data analyst programme balances introductory skills in Excel, SQL, and Python programming with practical studies in data visualisation, statistics, and machine learning. Together with practical projects, this syllabus will equip learners to become efficient data analysts with the skill of turning raw data into working insights that will be applied to make sound business decisions. As the role of data in all industries is constantly expanding, the competencies studied will provide a wide range of career options and precondition lifelong learning in analytics and data science.
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