Become a Data AnalystData Analytics Course in Ahmedabad

Overview of Data Analytics — definition and importance

Most data interview take-home tests include a messy CSV — candidates who can clean it quickly and correctly proceed; others do not

Applications in various industries (finance, healthcare, e-commerce, logistics)

Pivot tables and VLOOKUP are still tested in Excel rounds at consulting and banking firms — done at professional speed here

Career opportunities — Data Analyst, Business Analyst, MIS Analyst

Python Pandas is the tool used by 91% of data analysts in Indian IT companies — you start using it in Week 1, not Week 5

Data Analytics Process — collection, cleaning, analysis, visualisation, interpretation

Missing data handling strategies are a common interview question — knowing when to impute vs drop is a judgment skill this module builds

Descriptive Statistics — mean, median, mode, range, variance, standard deviation

GROUP BY, HAVING, and aggregate functions are tested in every data analyst SQL round — candidates who get them right move forward

Distribution types — normal, binomial, Poisson

JOINs (INNER, LEFT, RIGHT) are asked in every SQL interview — being able to explain the difference with a diagram is the expected standard

Inferential Statistics — hypothesis testing, confidence intervals, p-values

Window functions (ROW_NUMBER, RANK, LEAD, LAG) are what separate junior analysts from mid-level ones — most courses do not teach them at all

Probability distributions and Bayes' theorem

Subqueries and CTEs are how complex business questions are answered in SQL — the module covers both so you can write readable, maintainable queries

Sampling methods

Query optimisation (indexes, EXPLAIN ANALYSE) is asked at product companies and consulting firms — understanding it signals senior-level thinking

Data Collection Methods — surveys, APIs, web scraping

Dashboard design principles (correct chart type, colour for insight, not decoration) are what separate a useful dashboard from a confusing one — and interviewers can tell the difference immediately

Handling missing values and dealing with outliers

DAX in Power BI is the most-asked-about tool feature in analytics interviews — the module covers the formulas used in actual business reports

Data transformation

The "walk me through your dashboard" interview question requires you to tell a data story — this module teaches narrative structure for data presentations

Data normalisation and standardisation

Data refresh and live connections are what make dashboards useful in a production environment — covered from a business context here

Exploratory Data Analysis (EDA)

Descriptive statistics (mean, median, mode, standard deviation) are baseline — but interviewers ask you to interpret them in business context, not just calculate them

Data profiling

Correlation vs causation is a critical thinking test that appears in almost every data interview — having a clear, confident answer with an example is expected

Univariate and bivariate analysis

Hypothesis testing (t-test, chi-square) is how A/B test results are evaluated at product companies — understanding this makes you useful in product analytics roles

Data visualisation techniques — histograms, box plots, scatter plots

Normal distribution and its business implications are tested at consulting firms and in financial analytics roles — covered with practical business examples here

Tools: Excel, SQL, R, Python (Pandas, NumPy)

Principles of Data Visualisation

Linear regression is the most common model in business analytics — being able to interpret the output (not just run it) is what interviewers test

Choosing the right chart types

Classification models (logistic regression, decision trees) are used in credit scoring, churn prediction, and fraud detection — the industries most likely to hire you first

Design best practices

Model evaluation metrics (accuracy, precision, recall, F1) are asked in data science interviews and increasingly in analytics interviews too

Tableau — dashboards and reports

Feature engineering is where most of the value in ML comes from — this is the part that requires domain knowledge, which data analysts have over pure ML engineers

Power BI — data modelling and interactive dashboards

Matplotlib and Seaborn in Python

Excel interface, workbook and worksheet management

Structuring a data presentation for a business audience (not just showing charts) is what distinguishes analysts who get promoted from those who stay technical

Basic functions — SUM, AVERAGE, MIN, MAX

Resume and LinkedIn optimisation for data roles requires different keywords than other tech roles — covered here with placement team review

Cell referencing — relative, absolute, mixed

Portfolio hosting (GitHub for code, Kaggle for notebooks, Tableau Public for dashboards) is the standard expected by hiring managers — you will have all three set up

Data Import/Export (CSV, databases, web)

VLOOKUP, HLOOKUP, INDEX, MATCH

Logical functions — IF, AND, OR, NOT, nested IF

Date and Time functions — TODAY, NOW, DATEDIF, NETWORKDAYS

PivotTables, Pivot Charts and Data Analysis Toolpak

Conditional Formatting and Sparklines

What-If Analysis — Scenario Manager, Goal Seek, Data Tables, Solver

Introduction to Macros and VBA Programming

Power Query — importing, transforming, merging data

Integrating Excel with Power BI and Python

Overview of SQL — MySQL, PostgreSQL, SQL Server

SELECT, INSERT, UPDATE, DELETE

WHERE clause, logical and comparison operators, LIKE

ORDER BY, GROUP BY, HAVING

Aggregate functions — COUNT, SUM, AVG, MIN, MAX

String, Numeric, and Date/Time functions

INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, Self joins, Cross joins

Subqueries — in SELECT, FROM, WHERE clauses

Common Table Expressions (CTEs)

Data Manipulation and Transactions — BEGIN, COMMIT, ROLLBACK

Indexing and Query Optimisation

Stored Procedures, Triggers, and Views