What Will You Learn?
- Fundamentals of data analytics and data-driven decision-making.
- Microsoft Excel formulas, functions, PivotTables, and data cleaning.
- SQL queries, filtering, joins, grouping, and database fundamentals.
- Python programming for data analysis.
- Data manipulation using Pandas and NumPy.
- Data visualization using charts, graphs, and dashboards.
- Power BI fundamentals and interactive report development.
- Key performance indicators (KPIs) and business reporting.
- Exploratory data analysis and statistical fundamentals.
- Data validation, quality checks, and reporting accuracy.
- Practical data analysis projects and presentation of findings.
By the end of the internship, you can develop a stronger understanding of data analytics workflows and gain practical project experience to support your portfolio and future career development.
Data Analytics Internship Training Curriculum
Our Data Analytics Internship curriculum introduces essential analytical concepts and gradually progresses toward working with datasets, building dashboards, and generating actionable insights.
1. Introduction to Data Analytics
- Understanding data analytics and its importance.
- Types of data: structured and unstructured.
- Introduction to descriptive, diagnostic, predictive, and prescriptive analytics.
- Understanding the data analytics lifecycle.
- Identifying business problems and analytical objectives.
- Understanding datasets, variables, and data types.
- Introduction to data-driven decision-making.
- Exploring real-world applications of data analytics.
2. Microsoft Excel for Data Analysis
- Introduction to Excel and spreadsheet fundamentals.
- Working with rows, columns, tables, and data ranges.
- Using formulas and comon functions.
- Conditional formatting and data validation.
- Sorting, filtering, and removing duplicate records.
- Data cleaning and formatting techniques.
- Using lookup functions such as VLOOKUP and XLOOKUP.
- Working with IF, SUMIF, COUNTIF, and related functions.
- Creating PivotTables and PivotCharts.
- Building charts, summaries, and basic dashboards.
- Preparing reports using structured datasets.
3. SQL and Database Fundamentals
- Introduction to relational databases and SQL.
- Understanding tables, records, columns, and relationships.
- Writing SELECT queries.
- Filtering data using WHERE conditions.
- Sorting results with ORDER BY.
- Using aggregate functions such as SUM, COUNT, AVG, MIN, and MAX.
- Grouping data using GROUP BY and HAVING.
- Joining tables using INNER JOIN and LEFT JOIN.
- Working with subqueries and common table expressions where appropriate.
- Understanding NULL values and data validation.
- Using SQL queries to extract data for reports.
- Understanding database fundamentals and query accuracy.
4. Python Programming for Data Analytics
- Introduction to Python and its applications in analytics.
- Setting up a Python development environment.
- Variables, data types, operators, and expressions.
- Conditional statements and loops.
- Lists, tuples, dictionaries, and sets.
- Functions, modules, and reusable code.
- File handling and working with CSV files.
- Handling exceptions and common programming errors.
- Introduction to Jupyter Notebook.
- Writing Python scripts to process and analyze data.
5. Data Manipulation with Pandas and NumPy
- Introduction to Pandas and NumPy.
- Understanding Series and DataFrames.
- Importing CSV and Excel datasets.
- Inspecting data structure and summary statistics.
- Identifying and handling missing values.
- Removing duplicates and correcting data formats.
- Filtering, sorting, and selecting records.
- Grouping and aggregating datasets.
- Combining datasets using merge and concatenation.
- Working with dates and categorical data.
- Exporting processed datasets for reporting.
- Preparing clean and reliable data for analysis.
6. Data Visualization and Reporting
- Understanding the importance of data visualization.
- Choosing appropriate charts for different datasets.
- Creating bar charts, line charts, pie charts, and histograms.
- Understanding trends, distributions, and comparisons.
- Using charts to communicate analytical findings.
- Designing clear and readable reports.
- Applying appropriate labels, legends, and formatting.
- Identifying misleading visualizations and avoiding common mistakes.
- Presenting insights using visual summaries.
- Communicating analytical results to non-technical audiences.
7. Power BI and Interactive Dashboards
- Introduction to Microsoft Power BI.
- Understanding Power BI Desktop and its interface.
- Importing data from Excel, CSV, and supported databases.
- Cleaning and transforming data using Power Query.
- Managing data types, missing values, and duplicates.
- Understanding data relationships and data modeling.
- Creating tables, cards, charts, and slicers.
- Building interactive reports and dashboards
- Introduction to DAX measures and calculated columns.
- Working with common DAX functions.
- Creating KPI reports and performance summaries.
- Publishing and sharing reports where supported and permitted.
8. Exploratory Data Analysis and Statistics
- Understanding exploratory data analysis (EDA).
- Calculating mean, median, mode, and range.
- Understanding variance and standard deviation.
- Exploring distributions, outliers, and data patterns.
- Identifying trends and relationships between variables.
- Understanding correlation and its limitations.
- Comparing categories and analyzing changes over time.
- Formulating analytical questions and hypotheses.
- Interpreting findings without overstating conclusions.
- Documenting observations and analytical assumptions.
9. Business Intelligence and KPI Reporting
- Understanding business intelligence fundamentals.
- Identifying key performance indicators.
- Preparing sales and revenue reports.
- Analyzing customer and product performance.
- Understanding operational and financial reporting concepts.
- Comparing performance across periods and categories.
- Tracking targets and performance metrics.
- Building summary reports for decision-makers.
- Identifying opportunities for further investigation.
- Presenting findings with clear recommendations.
10. Data Quality, Testing, and Documentation
- Understanding data quality and accuracy.
- Validating records and identifying inconsistencies.
- Checking missing values, duplicates, and incorrect formats.
- Comparing source data with report outputs.
- Documenting data cleaning and transformation steps.
- Maintaining reproducible analysis workflows.
- Organizing files, scripts, queries, and reports.
- Using Git for version control where appropriate.
- Preparing clear project documentation.
- Following responsible data handling and privacy practices.
11. Practical Data Analytics Project
- Understanding project requirements and business questions.
- Selecting an appropriate dataset for analysis.
- Importing and reviewing raw data.
- Cleaning and preparing data for analysis.
- Using Excel, SQL, or Python to explore datasets.
- Identifying trends, patterns, and important metrics.
- Creating charts and interactive Power BI dashboards.
- Preparing a report with key findings.
- Presenting conclusions and recommendations.
- Documenting the project for portfolio use.
Example projects may include sales performance analysis, customer behavior analysis, inventory reporting, employee data analysis, or business KPI dashboards. Project selection depends on the curriculum and available datasets.
The curriculum can be adapted according to your existing knowledge, learning progress, project requirements, and the internship structure offered by Akash Technolabs.