Data Analyst Course Syllabus 2025: Learn SQL, Python, Excel & More
Data Analyst Course Syllabus 2025: Businesses and organizations rely on the insights of data analysts to make better decisions, be more productive, and enhance customer experiences, which leads to an increased demand for competent data analysts.
E&ICT Academy in IIT Kanpur provides the full data analytics course that endows the learners with abilities and knowledge to aid them in making a successful entry into the cutthroat data analytics sector. Principal topics covered by the course material are SQL, Python, Excel, data visualization, and machine learning so the students can face the world fully equipped with the capabilities to start work as soon as they graduate.
Data Analyst Course Benefits and Learning Outcomes
The E&ICT Academy, IIT Kanpur, provides students with a balanced learning experience in the Data Analyst Course, providing them with the technical knowledge they need. Learners will have expertise with machine learning tools, SQL, Python, and Excel, which will enable them to organize, analyze, and present complex datasets easily. With a focus on practical application, the course ensures that students can consistently translate data into insights that can be applied.
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The program’s real-world projects and industry case studies will prepare graduates for successful careers in numerous industries, including technology, marketing, healthcare, and finance. A successful data analyst with the ability to make informed data-driven decisions that enhance corporate performance may be achieved through this training.
Core Modules in the Data Analyst Course
The following are the core models of this course:
Overview of Data Analytics
- Understanding Data Analytics: Acquire knowledge of the core ideas, uses, and practical implications of data analytics.
- Data sources: Data collecting techniques, and organized versus unstructured data types.
- Career Scope: Information about several career pathways, data analyst-hiring sectors, and prospects for future advancement.
- Business Analytics Integration: Examine how business analytics is essential to data-driven decision-making through business analytics integration.
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Excel for Data Analysis
- Basic Excel functions: Include pivot tables, filtering, sorting, and basic formulae.
- Advanced Excel Techniques: Power Query, VBA, macros, and sophisticated data visualization methods.
- Data Cleaning & Preprocessing: Data transformation, outlier identification, and missing data handling are all included in data cleaning and preprocessing.
SQL and Database Management
- Overview of SQL: Knowledge of databases, SQL procedures, and query creation.
- Data manipulation: Using commands like SELECT, INSERT, UPDATE, and DELETE.
- Joins and Subqueries: Creating intricate queries and interacting with several tables.
- Normalization of databases: Effective methods for storing and retrieving data.
Python for Data Analytics
- Basics of Python: Loops, functions, data types, and variables.
- Pandas Data Manipulation: Datasets may be imported, cleaned, and transformed.
- Using NumPy and SciPy for Data Analysis: Carrying out numerical calculations.
- Overview of AI and ML: To improve your predictive analytics abilities, comprehend the fundamental ideas presented in the Artificial Intelligence and Machine Learning Course.
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Advanced Topics in the Course
Statistics and Probability for Data Analysis
- Descriptive Statistics: Distributions, mean, standard deviation, median, and mode.
- Statistics that are inferred: Confidence intervals, regression analysis, and hypothesis testing.
- Ideas in Probability: Random variables, probability distributions, and the Bayes Theorem.
Data Visualization Tools
- Overview of Power BI & Tableau: Generating dynamic reports and dashboards.
- Top Techniques for Visualizing Data: Selecting the appropriate chart format, using data to convey a story, and staying clear of frequent traps.
- Interactive Case Studies: Using visualization approaches to solve practical business issues.
Introduction to Machine Learning
- Supervised vs. Unsupervised Learning: Predictive modeling fundamentals.
- Feature Engineering: Improving model accuracy by choosing the appropriate variables.
- Measures for Model Evaluation: F1-score, accuracy, recall, and precision.
- Case Studies and Real-World Uses: Situations in which machine learning improves data analysis in practical business settings.
Capstone Project and Hands-on Learning
- Sector-Specific Initiatives: Using case studies in marketing, finance, healthcare, and business to apply newly acquired principles.
- Last Capstone Project: A thorough project that shows how to gather, clean, analyze, visualize, and report data.
- Building a Portfolio: Displaying finished work to further one’s profession.
Additional Learning Opportunities
Applications of Data Analytics in the Real World
The course involves actual business case studies from the fields of marketing, healthcare, and finance, wherein data analytics are employed to support key business decisions.
Industry insights and networking
Students will receive the chance to attend networking events and guest lectures from successful leaders in the business.
Assistance with internships and placements
The program provides real-life internship experiences and helps in securing jobs with top companies.
Examining AI and ML for Data Analysis
The Artificial Intelligence and Machine Learning Course provide advanced knowledge of automation and predictive modelling for those interested in exploring further AI-driven analytics.
Who Should Enroll in This Course?
This course will be of great use to a broad audience of learners, which makes it the perfect option for anyone seeking to start or grow their career as a data analyst. The organized syllabus and hands-on methodology will serve future data analysts who wish to practice in the field.
Individuals can take the program without technical training and want to advance their careers by enhancing their data analysis and visualization skills. This program is also available to students and recent graduates who wish to acquire business-relevant skills and improve employability should they desire to work in data-driven roles.
Finally, managers and business professionals can take this training to become more skilled in making decisions based on data insights. With the right insights, they can fine-tune operations, make sharper calls, and keep their businesses running smoothly.
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Conclusion
Gaining proficiency in SQL, Python, Excel, and visualization tools can put you on the path to success in a career in data analytics, which has enormous potential. The E&ICT Academy’s Data Analyst Course at IIT Kanpur offers an organized educational experience that guarantees you acquire both technical know-how and real-world experience.
The Business Analytics Program is another option for individuals who want to specialize deeper and build enterprise-specific strategic analytical abilities. This program provides you with the in-demand skills that will lead you to excel in a competitive job market, whether you’re starting from ground zero or advancing your skills for career promotion.
Sign up today to get started on your path of a successful data analytics professional!w
