Data Analytics Course in Delhi  with 100% Job Support

Descriptive analytics

Diagnostic analytics

Predictive analytics

Prescriptive analytics

Revenue

Profit margin

Conversion rate

Retention rate

Churn rate

Customer lifetime value

Average order value

Return on ad spend

Interface, cell formatting, data types, sorting, filtering, data validation, named ranges, and conditional formatting

SUM, COUNT, MIN, MAX, AVERAGE, COUNTA, COUNTBLANK

IF, AND, OR, NOT

SUMIF, SUMIFS, COUNTIF, COUNTIFS

XLOOKUP, INDEX, MATCH

FILTER, SORT, UNIQUE

LEFT, RIGHT, MID, FIND, LEN, TRIM, SUBSTITUTE, UPPER, LOWER, PROPER

TODAY, NOW, MONTH, YEAR, DAY, WEEKDAY, NETWORKDAYS, WEEKNUM

Removing duplicates, handling blanks, standardizing text, splitting and combining columns, cleaning dates, and preparing sales/customer data

Grouping, binning, calculated fields, slicers, timelines, summary reports, and cross-tab reports

Importing from Excel/CSV/folders, removing and renaming columns, changing data types, merging, appending, and creating reusable cleaning workflows

Dashboard design, KPI cards, trend charts, bar charts, waterfall charts, dynamic dashboards, and executive layouts

Relational databases, tables, primary keys, foreign keys, data types, SELECT, WHERE, ORDER BY, GROUP BY, HAVING, DISTINCT, and NULL handling

INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, UNION, UNION ALL, INTERSECT, and EXCEPT

CASE WHEN, date functions, text functions, conditional aggregations, deduplication, rollups, and revenue calculations

Subqueries, CTEs, temporary tables, window functions, ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running totals, and moving averages

Cohort analysis, funnel analysis, retention analysis, segmentation, churn-risk identification, RFM analysis, booking window analysis, attribution basics, data quality checks, and query optimization basics

Google BigQuery, Snowflake, Microsoft Fabric Warehouse, Amazon Redshift, or Databricks SQL

Transactional vs analytical data, OLTP vs OLAP, fact tables, dimension tables, star schema, snowflake schema, table grain, primary keys, and foreign keys

Raw layer, staging layer, intermediate layer, mart layer, metric layer, documentation, testing, and data lineage

Customer dimension, product dimension, date dimension, sales fact table, marketing campaign fact table, web events fact table, and aggregated reporting tables

Null checks, duplicate checks, referential integrity, freshness checks, volume checks, outlier checks, and metric reconciliation

Why Git matters, GitHub basics, project folder structure, SQL file organization, and README documentation

Power BI Desktop, Power BI Service, import mode, DirectQuery, data view, model view, and report view

Cleaning data, merging, appending, conditional columns, type changes, date transformations, text transformations, and parameterized queries

Star schema, fact/dimension tables, relationships, cardinality, filter direction, date tables, role-playing dimensions, and measure tables

Calculated columns vs measures, SUM, COUNT, DISTINCTCOUNT, CALCULATE, FILTER, ALL, DIVIDE, time intelligence, YTD, MTD, QTD, rolling averages, ranking, contribution percentage, row context, and filter context

KPI cards, trend charts, bar charts, matrix visuals, drill-through pages, tooltips, bookmarks, field parameters, slicers, filters, and executive summary pages

Publishing, workspaces, scheduled refresh, gateway basics, row-level security, app sharing, deployment pipelines, Performance Analyzer, and dataset optimization

Clean semantic model design, business-friendly field names, measure descriptions, and consistent metric definitions

Interface, data connections, dimensions and measures, discrete vs continuous fields, marks card, shelves, filters, and parameters

Calculated fields, logical calculations, string calculations, date calculations, table calculations, percent of total, running total, rank, and Level of Detail expressions

Bar charts, line charts, scatter plots, heatmaps, maps, dual-axis charts, combo charts, reference lines, trend lines, and forecasting basics

Layouts, containers, actions, filters, parameters, story points, executive storytelling, and avoiding dashboard clutter

Installation, Jupyter Notebook, VS Code, variables, data types, strings, lists, tuples, dictionaries, sets, conditionals, loops, functions, lambda functions, comprehensions, error handling, files, modules, and packages

Virtual environments, package installation, project folder structure, reading CSV/Excel/JSON files, connecting to APIs, and writing reusable scripts

Arrays, indexing, slicing, reshaping, combining arrays, math operations, and basic statistics

Series, DataFrames, reading data, inspecting data, filtering rows, selecting columns, renaming, dropping, missing values, duplicates, type conversion, dates, grouping, merging, appending, pivots, and derived columns

Summary statistics, frequency tables, univariate analysis, bivariate analysis, correlation, outliers, missing values, and business interpretation

Matplotlib, Seaborn, line charts, bar charts, histograms, box plots, scatter plots, heatmaps, regression plots, and geospatial basics with Folium

Automating repeated reports, cleaning multiple files from a folder, exporting clean datasets, creating automated Excel outputs, and report automation concepts

Population vs sample, mean, median, mode, variance, standard deviation, percentiles, distributions, skewness, and outliers

Probability concepts, conditional probability, expected value, normal distribution, and binomial distribution

Sampling, sampling bias, confidence intervals, hypothesis testing, p-values, statistical significance, and practical significance

A/B testing, control vs treatment, conversion rate testing, t-test, chi-square test, ANOVA basics, sample size intuition, experiment design, and common testing mistakes

Simple linear regression, multiple linear regression, coefficients, R-squared, residuals, and correlation vs causation

Supervised learning, unsupervised learning, regression, classification, clustering, train/test split, features, and target variables

Linear regression, decision tree regression, random forest regression, RMSE, MSE, MAE, and business interpretation of errors

Logistic regression, decision tree classifier, random forest classifier, KNN, accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC basics

Feature engineering, categorical variables, scaling, cross-validation, grid search, overfitting, and underfitting

K-means clustering, PCA, customer segmentation, and cluster interpretation

Saving models with pickle, simple Streamlit apps, model documentation, and business recommendations from model outputs

Bias in data, explainability, model limitations, human review, and monitoring model performance

LLMs, GPT-style models, tokens, context windows, prompt engineering, system prompts, user prompts, few-shot prompting, hallucination risk, and bias risk

Using AI to understand business problems, generate SQL drafts, debug SQL, explain SQL, write Python, debug Python, summarize EDA, design dashboards, and generate stakeholder summaries

API basics, API keys conceptually, request/response structure, temperature, max tokens, cost awareness, prompt templates, and structured outputs

Embeddings, semantic search, FAISS, vector databases, similarity search, and analytics use cases

Retrieval-Augmented Generation, chunking, metadata, retrieval quality, grounded answers, source citations, RAG evaluation, and preventing unsupported answers

Tool use, planning, SQL agents, dashboard assistants, documentation assistants, data quality assistants, risks of agentic workflows, and human-in-the-loop review

Data privacy, sensitive data handling, hallucination checks, governance, explainability, and when not to use AI

Data warehouse, data lake, lakehouse, data mart, batch processing, streaming basics, ELT vs ETL, and bronze/silver/gold layers

Google BigQuery, Snowflake, Microsoft Fabric, Databricks, or Amazon Redshift

Loading CSV files, Excel files, API data, scheduled refresh, and data pipeline basics

SQL transformations, Python transformations, Power Query transformations, and dbt-style transformation logic

Access control, data privacy, query cost, storage cost, refresh frequency, documentation, and lineage

Campaign performance, CTR, CPC, CPA, ROAS, funnel conversion, attribution basics, and lead scoring

Revenue, pipeline analysis, win rate, sales cycle, salesperson performance, and forecasting basics

Segmentation, RFM, customer lifetime value, churn, retention, and repeat purchase rate

DAU, MAU, activation, feature adoption, retention curves, funnel drop-off, and North Star metric

Revenue, cost, gross margin, EBITDA basics, budget vs actual, and forecasting

Turnaround time, SLA, defect rate, inventory, process bottlenecks, and productivity metrics

SQL business analysis

Power BI executive dashboard

Python EDA project

Statistics or A/B testing project

Machine learning project

GenAI analytics project

Problem statement, dataset used, methodology, tools used, key findings, business recommendations, limitations, and next steps