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📚 Curriculum (8 Units)
Unit 1: Data Analytics Foundations & Python Setup 20 topics
What is Data Analytics & Why It Matters in 2025
Descriptive, diagnostic, predictive, prescriptive analytics explained with real business examples. How companies like Amazon, Swiggy, Zepto use data daily. Job market and salary data for India.
Types of Data — Structured, Unstructured, Semi-structured
CSV, Excel, SQL tables (structured). Images, videos, text (unstructured). JSON, XML, logs (semi-structured). Which tools handle each type.
Data Lifecycle — Collection to Insight
Data collection → storage → cleaning → processing → analysis → visualization → decision. Real pipeline walkthrough.
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Statistics Fundamentals — Central Tendency
Mean, median, mode. When to use each. Weighted mean. Trimmed mean. Practical: calculate for a sales dataset.
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Statistics Fundamentals — Spread & Distribution
Variance, standard deviation, range, IQR. Normal distribution and bell curve. Z-score and what it tells you.
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Statistics Fundamentals — Correlation & Probability
Pearson correlation coefficient. Positive/negative/no correlation. Basic probability rules. Conditional probability. Bayes theorem intuition.
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Python Setup for Data Science
Anaconda distribution install, Jupyter Notebook, JupyterLab, VS Code with Jupyter extension. Virtual environments for data projects. pip install essentials.
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Python Basics for Analysts — Variables & Data Types
Variables, int, float, str, bool, None. Type casting. Arithmetic operators. String formatting with f-strings. print() formatting.
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Python Basics — Lists, Tuples & Dictionaries
List operations: append, extend, slicing, list comprehension. Tuples for immutable data. Dictionary: keys, values, nested dicts.
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Python Basics — Loops & Functions
for loop with range/enumerate/zip. while loop. def functions, return values, *args, **kwargs. Lambda functions for quick transforms.
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Python Basics — File Handling for Analysts
Read/write CSV with open(). csv module: DictReader, DictWriter. Read JSON files. Read Excel with openpyxl. Pathlib for file paths.
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Python Basics — Exception Handling
try/except/finally for file and API errors. Common exceptions: FileNotFoundError, KeyError, ValueError. Writing robust data scripts.
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AI Tools for Data Analysts — Overview
ChatGPT for data explanations. Claude AI for code review. GitHub Copilot for writing pandas queries. Julius AI for chat-with-data. Perplexity for research.
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Prompt Engineering for Data Work
Zero-shot and few-shot prompting for data tasks. Prompting for SQL queries, pandas code, chart recommendations. Chain-of-thought for analysis.
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Google Colab — Cloud Notebooks
Colab interface, connecting to Google Drive, installing packages, GPU runtime for ML. Sharing notebooks. Colab + ChatGPT workflow.
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Business Understanding — Asking the Right Questions
Framing business problems as data questions. KPIs vs metrics. SMART questions. Stakeholder communication. A/B test mindset.
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Data Ethics & Privacy
GDPR basics, data anonymization, bias in data, responsible AI. Why clean data matters. Consequences of bad data decisions.
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Industry Use Cases — Sector by Sector
How data analytics is used in: e-commerce, fintech, healthcare, logistics, ed-tech, retail, banking. Real datasets from each.
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Career Roadmap — Data Analyst to Data Scientist
Roles: Data Analyst → Sr. Analyst → Data Scientist → ML Engineer. Required skills per level. Top companies hiring in India. Resume keywords.
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Practice Exercise — Unit 1
Analyze a business scenario: given raw sales data (CSV), answer 10 business questions using Python basics. Use ChatGPT to explain your findings in plain English.
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Unit 2: Python for Data Analysis — NumPy & Pandas 22 topics
NumPy Introduction — Why Faster Than Python Lists
ndarray vs Python list. Memory layout. Vectorized operations avoid loops. Broadcasting concept. When to use NumPy.
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NumPy — Array Creation & Attributes
np.array(), np.zeros(), np.ones(), np.arange(), np.linspace(), np.random.rand(). shape, dtype, ndim, size, itemsize.
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NumPy — Indexing, Slicing & Reshaping
1D/2D array indexing. Boolean indexing. Fancy indexing. reshape(), flatten(), ravel(), transpose(), np.newaxis.
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NumPy — Mathematical & Statistical Operations
np.sum(), np.mean(), np.median(), np.std(), np.min(), np.max() with axis parameter. np.percentile(). np.corrcoef().
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NumPy — Broadcasting & Vectorized Operations
Broadcasting rules for different shapes. Vectorized arithmetic. np.where() for conditional logic. np.clip(), np.abs().
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Pandas Introduction — Series & DataFrame
pd.Series vs pd.DataFrame. Creating from lists, dicts, arrays. index and columns. dtypes overview. Why Pandas is the #1 data tool.
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Pandas — Reading Data
pd.read_csv() with all key parameters (sep, header, index_col, usecols, dtype, na_values, nrows). read_excel(), read_json(), read_sql().
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Pandas — Inspecting & Exploring Data
df.head(), tail(), shape, dtypes, info(), describe(). value_counts(). nunique(). isnull().sum(). Exploratory first steps.
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Pandas — Selecting & Filtering Data
df["col"], df[["col1","col2"]]. df.loc[] (label-based). df.iloc[] (position-based). Boolean filtering. query() method.
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Pandas — Data Cleaning — Missing Values
isnull(), notnull(). dropna() with thresh, subset. fillna() with value, method (ffill, bfill). Interpolate(). When to drop vs fill.
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Pandas — Data Cleaning — Duplicates & Types
drop_duplicates(). astype() for type conversion. pd.to_datetime(), pd.to_numeric(). strip() and str methods. Fixing dirty column names.
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Pandas — Data Transformation
apply() with lambda. map() for value replacement. replace(). rename(). assign() for new columns. cut() and qcut() for binning.
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Pandas — String Operations
df["col"].str.lower/upper/strip/split/contains/replace/extract. Regex with str.extract(). Splitting one column into multiple.
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Pandas — GroupBy & Aggregation
groupby() with agg({col: func}). Multiple aggregations. named aggregation. transform() for group-level calculations. pivot_table().
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Pandas — Merging & Joining DataFrames
merge() with how: inner/left/right/outer. on, left_on, right_on. join(). concat() with axis. Handling duplicate column names after merge.
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Pandas — Time Series Analysis
pd.to_datetime(). DatetimeIndex. resample() for weekly/monthly aggregation. rolling() for moving averages. shift() for lag features.
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Pandas — Sorting, Ranking & Window Functions
sort_values(), sort_index(), nlargest(), nsmallest(). rank(). cumsum(), cumprod(), cummax(). expanding() window.
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Pandas — MultiIndex & Advanced Features
MultiIndex creation and access. stack()/unstack(). melt() for wide-to-long. explode() for list columns. pd.Categorical for memory efficiency.
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AI-Assisted Data Cleaning with Claude & Copilot
Use Claude AI to suggest cleaning strategies for messy datasets. GitHub Copilot to write pandas cleaning code. Prompt patterns for data issues.
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pandas-ai — Chat with Your DataFrame
Install pandas-ai. SmartDataframe() wrapper. Ask natural language questions: "What is the average sales by city?" Real demo with a business dataset.
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Performance Tips — Large DataFrames
chunking with chunksize. Dask for out-of-memory data. Polars as fast alternative. dtype optimization (int8 vs int64). read_csv() with dtypes.
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Practice Exercise — Unit 2
Clean and analyze a real e-commerce dataset (10,000 rows): handle missing values, fix dtypes, GroupBy analysis, time series trend, merge two tables. Answer 15 business questions.
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Unit 3: Data Visualization — Matplotlib, Seaborn & Plotly 20 topics
Data Visualization Principles
Why visualization matters. Choosing the right chart: bar for comparison, line for trend, scatter for correlation, pie for proportion. Edward Tufte principles. Chart junk.
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Matplotlib — Figure, Axes & Basic Charts
Figure and Axes architecture. plt.plot(), plt.bar(), plt.barh(), plt.scatter(), plt.hist(). Titles, labels, legends, grid.
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Matplotlib — Subplots & Advanced Customization
plt.subplots(nrows, ncols), sharex/sharey. Custom colors, linestyles, markers. Annotations with ax.annotate(). Saving figures (savefig, dpi, tight_layout).
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Seaborn — Statistical Visualization
seaborn philosophy: high-level over matplotlib. sns.histplot(), boxplot(), violinplot(), kdeplot(). Context: paper/talk/poster.
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Seaborn — Categorical Charts
sns.barplot(), countplot(), stripplot(), swarmplot(), catplot(). hue parameter for grouping. Order parameter.
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Seaborn — Relationship & Distribution Charts
sns.scatterplot(), lineplot(), regplot(), lmplot(). pairplot() for EDA. heatmap() for correlation matrix.
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Plotly — Interactive Charts
plotly.express: px.bar(), px.line(), px.scatter(), px.histogram(). Hover tooltips. Animation with frames. Export to HTML.
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Plotly Dash — Interactive Dashboards
Dash app structure (layout + callbacks). dcc.Graph, dcc.Dropdown, dcc.Slider. @app.callback with Input/Output. Deploy to Render.
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Chart Types Deep Dive — When to Use What
Bar vs column. Grouped vs stacked bar. 100% stacked. Waterfall chart. Funnel chart. Treemap. Bullet chart. Choosing for business reports.
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EDA — Exploratory Data Analysis Workflow
Full EDA framework: shape → dtypes → missing → distributions → outliers → correlations → business insights. Using ydata-profiling (pandas-profiling) for instant EDA report.
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Outlier Detection & Treatment
IQR method. Z-score method. Box plots for visual outlier detection. When to remove vs cap vs keep outliers. Impact on ML models.
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Correlation Analysis
Pearson, Spearman, Kendall correlation. Heatmap interpretation. Multicollinearity concept. Correlation vs causation.
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Storytelling with Data
Data storytelling framework: context → insight → recommendation. Building a narrative. Slide design for data presentations. Annotation-driven charts.
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AI-Assisted Chart Generation
Using ChatGPT and Claude to suggest charts for a given dataset. Copilot to write matplotlib/seaborn code from description. LIDA for AI-generated visualizations.
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LIDA — AI Visualization Tool
Microsoft LIDA for automatic chart generation from data. Setting up LIDA. Generating multiple chart options with natural language. Editing AI charts.
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Color Theory for Data Visualization
Sequential, diverging, qualitative palettes. Colorblind-safe palettes (ColorBrewer). Seaborn palette options. Matplotlib colormap guide.
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Building a Full EDA Report with AI
Combine: ydata-profiling for auto EDA + Claude for interpretation + LIDA for charts + Jupyter notebook as deliverable. Professional analyst workflow.
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Geospatial Visualization — Folium & Plotly Maps
folium for interactive maps. Choropleth maps with Plotly. Plotting store locations, delivery routes, regional sales. India district maps.
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Word Clouds & NLP Visualization
wordcloud library. Sentiment visualization. Topic distribution charts. Visualizing customer review data.
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Practice Exercise — Unit 3
Build a full EDA notebook for a retail dataset: 10 different chart types, correlation heatmap, outlier analysis, interactive Plotly dashboard, storytelling narrative with AI-written insights.
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Unit 4: SQL & Excel for Data Analysts 20 topics
SQL for Analysts — Setup & Basics
Install MySQL Workbench / DBeaver / Oracle SQL Developer. SELECT, FROM, WHERE, ORDER BY, DISTINCT, LIMIT. Column aliases.
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SQL — Filtering & Pattern Matching
WHERE with =,!=,,BETWEEN,IN,NOT IN,IS NULL,LIKE %, LIKE _. AND/OR/NOT logic. NULL handling with COALESCE.
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SQL — Aggregate Functions
COUNT(*), COUNT(col), SUM, AVG, MIN, MAX. GROUP BY with multiple columns. HAVING to filter groups. ROUND().
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SQL — JOINs for Analysts
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN. Self JOIN. Multiple table JOINs. Common analyst join patterns (orders + customers + products).
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SQL — Subqueries & CTEs
Scalar subquery, correlated subquery, IN with subquery. WITH clause (CTE). Multiple CTEs. When CTEs improve readability.
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SQL — Window Functions for Analysts
ROW_NUMBER(), RANK(), DENSE_RANK(). Running totals: SUM() OVER (PARTITION BY ORDER BY). LAG() and LEAD() for period comparisons. NTILE() for quartiles.
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SQL — Date & String Functions
DATE_FORMAT(), DATEDIFF(), DATE_ADD(), EXTRACT(). CONCAT(), SUBSTRING(), UPPER(), LOWER(), TRIM(), REPLACE(). Oracle: TO_CHAR(), TO_DATE().
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SQL — Real Analyst Queries
Cohort analysis in SQL. Customer lifetime value query. Retention rate calculation. Top-N per group with ROW_NUMBER(). Month-over-month growth.
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SQL + Python — pandas read_sql()
Connect pandas to MySQL/PostgreSQL/Oracle with SQLAlchemy. pd.read_sql(query, engine). Parameterized queries. Write results back with to_sql().
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AI-Powered SQL — Text to SQL
Using ChatGPT and Claude to generate SQL from plain English descriptions. Validating AI-generated SQL. SQLCoder model. Prompt patterns for complex joins.
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Excel for Analysts — Essentials
Excel interface, keyboard shortcuts, cell references (absolute $A$1 vs relative). Named ranges. Table (Ctrl+T) and structured references.
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Excel — Lookup Functions
VLOOKUP (column index, exact/approx match). XLOOKUP (modern replacement). INDEX-MATCH for flexible lookups. MATCH standalone use.
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Excel — Logical & Text Functions
IF, IFS, IFERROR, AND, OR, NOT. COUNTIF, COUNTIFS, SUMIF, SUMIFS, AVERAGEIF. TEXT, LEFT, RIGHT, MID, FIND, TRIM, CONCAT.
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Excel — PivotTables & PivotCharts
Creating PivotTables, dragging fields, grouping dates, calculated fields. Slicers for filtering. PivotChart. Refreshing data.
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Excel — Dynamic Arrays (Modern Excel)
FILTER(), SORT(), SORTBY(), UNIQUE(), SEQUENCE(). Spill range behavior. XLOOKUP with multiple results. LET() for named calculations.
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Excel — Power Query for Data Cleaning
Power Query editor: import CSV, filter rows, split columns, unpivot, merge queries, append queries. M language basics. Refresh pipeline.
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Excel Dashboards — Building Exec Reports
Dashboard design: KPI cards, sparklines, dynamic charts linked to slicers. Conditional formatting for heatmaps. Print-ready layout.
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Excel + AI — Copilot in Excel & Formula AI
Microsoft Copilot in Excel for auto-analysis. Formula AI tools: Excel Formula Bot, Formulabot.com. Asking ChatGPT to fix Excel formulas.
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Google Sheets for Analysts
QUERY() function (SQL-like). IMPORTDATA(), IMPORTRANGE(). Google Sheets API with Python. Collaborating on live data. Looker Studio connection.
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Practice Exercise — Unit 4
Write 20 SQL queries on a HR + Sales database (window functions, CTEs, cohort analysis). Build a management Excel dashboard with PivotTables, XLOOKUP, dynamic charts and slicers.
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Unit 5: Power BI & Tableau — Business Dashboards 18 topics
Power BI Introduction & Interface
What is Power BI, Desktop vs Service vs Mobile. Interface: Report view, Data view, Model view. Import vs DirectQuery vs Live Connection.
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Power BI — Connecting Data Sources
Connect to: Excel, CSV, SQL Server, MySQL, Web, SharePoint, Oracle. Power Query Editor for transformation before loading.
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Power BI — Power Query Transformations
Filter, sort, split column, unpivot, pivot, merge queries, append. Data type changes. Custom column with M formula basics.
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Power BI — Data Modeling & Relationships
Star schema vs snowflake. Creating relationships (1:many, many:many). Cross-filter direction. Role-playing dimensions.
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Power BI — DAX Basics
DAX vs M language. Calculated columns vs measures. SUM, COUNT, AVERAGE, DISTINCTCOUNT, DIVIDE. CALCULATE() fundamentals.
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Power BI — DAX Advanced
Time intelligence: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD. RANKX(). SUMX() iterator. Variables with VAR...RETURN. FILTER().
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Power BI — Visuals & Report Design
Bar, line, card, KPI, matrix, table, map, donut visuals. Format pane. Conditional formatting. Report themes. Mobile layout.
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Power BI — Slicers, Filters & Drill-through
Slicer types (list, dropdown, between). Page filters vs visual filters. Drill-through page. Drill-down in hierarchy visuals.
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Power BI — Publishing & Service
Publish to Power BI Service. Workspaces. Row-Level Security (RLS). Scheduled refresh. Dashboard vs Report difference. Sharing.
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Power BI — AI Visuals
Q&A visual (natural language queries). Key Influencers visual. Decomposition Tree. Anomaly Detection on line charts. Smart Narratives.
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Power BI + Copilot
Microsoft Copilot in Power BI: auto-generate reports, write DAX measures with natural language, summarize report pages. Practical demo.
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Tableau Introduction & Interface
Tableau Desktop interface. Dimensions vs Measures. Discrete vs Continuous. Connecting to data. Worksheet vs Dashboard vs Story.
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Tableau — Building Charts
Bar, line, scatter, map, heatmap (highlight table), treemap, pie, bullet chart. Drag-drop fields. Marks card.
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Tableau — Calculated Fields & LOD
Calculated fields with IF/CASE/IIF. Table calculations: RUNNING_SUM, WINDOW_AVG, RANK. LOD expressions: FIXED, INCLUDE, EXCLUDE.
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Tableau — Dashboards & Stories
Dashboard layout: floating vs tiled. Actions (filter, highlight, URL). Story points for presentation. Tableau Public for sharing.
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Looker Studio (Google Data Studio)
Connect Google Analytics, Google Ads, BigQuery, Google Sheets. Calculated metrics. Date range controls. Community connectors.
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Choosing the Right BI Tool
Power BI vs Tableau vs Looker Studio vs Metabase. Cost, learning curve, integrations, job market demand. India company usage.
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Practice Project — BI Dashboard
Build a full Sales Performance dashboard in Power BI: data model with 4 tables, 10 DAX measures, interactive slicers, drill-through, RLS, published to Power BI Service.
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Unit 6: Machine Learning for Analysts 20 topics
Machine Learning Introduction for Analysts
What is ML vs traditional programming. Supervised, unsupervised, reinforcement learning. When ML helps a business vs when simple stats suffice.
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ML Workflow — From Data to Deployed Model
Problem definition → data collection → EDA → feature engineering → model selection → training → evaluation → deployment. Full pipeline overview.
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Feature Engineering
Creating new features from existing. Date features (day of week, month, quarter). Label encoding, one-hot encoding. Scaling: StandardScaler, MinMaxScaler. Handling skewed data.
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Train-Test Split & Cross Validation
train_test_split(). Why we split data. K-fold cross-validation. Stratified k-fold. Data leakage and how to prevent it.
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Linear Regression
Simple and multiple linear regression. Coefficients interpretation. R-squared and adjusted R-squared. Assumptions: linearity, normality, no multicollinearity.
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Logistic Regression — Classification
Binary classification. Sigmoid function. Probability threshold. Confusion matrix, accuracy, precision, recall, F1-score, ROC-AUC.
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Decision Trees & Random Forest
Decision tree: entropy, information gain, Gini impurity. Overfitting with deep trees. Random Forest: bagging, feature importance. When to use.
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Gradient Boosting — XGBoost & LightGBM
Boosting concept. XGBoost: key hyperparameters. LightGBM: faster for large data. Handling missing values natively. Feature importance.
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Scikit-learn Pipeline
Pipeline() to chain preprocessing + model. ColumnTransformer for mixed feature types. GridSearchCV for hyperparameter tuning. Cross-validated pipeline.
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Model Evaluation — Regression Metrics
MAE, MSE, RMSE, MAPE, R-squared. Residual plots. When RMSE vs MAE is better. Business interpretation of metrics.
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Model Evaluation — Classification Metrics
Confusion matrix deep dive. Precision vs recall trade-off. F1-score. ROC curve. AUC score. When to optimize precision vs recall.
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Clustering — K-Means & DBSCAN
K-Means: centroids, elbow method for K selection, silhouette score. DBSCAN for arbitrary shapes. Customer segmentation use case.
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Dimensionality Reduction — PCA
Curse of dimensionality. PCA: explained variance, choosing components. t-SNE for visualization. Use cases in analytics.
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Time Series Forecasting
Decomposition: trend, seasonality, residual. ARIMA basics. Prophet by Meta for business forecasting. Evaluating with MAPE.
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Customer Churn Prediction Project
End-to-end project: telecom churn dataset. Feature engineering, class imbalance with SMOTE, XGBoost model, SHAP for explanability, business recommendations.
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SHAP — Explainable AI for Analysts
SHAP values for model interpretation. shap.summary_plot(), force_plot(), waterfall plot. Making ML results understandable to business stakeholders.
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AutoML — H2O, PyCaret & Auto-sklearn
AutoML concept: automated feature engineering + model selection + hyperparameter tuning. PyCaret in 5 lines of code. H2O AutoML. When to use AutoML.
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AI-Assisted ML with Claude & Copilot
Use Claude AI to select the right algorithm for a business problem. Copilot to write scikit-learn pipelines. ChatGPT to interpret model results for stakeholders.
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ML Model Deployment Basics
Saving models with joblib/pickle. Flask API to serve predictions. Streamlit for quick ML apps. Hugging Face Spaces for free hosting.
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Practice Project — House Price Prediction
Full ML project: EDA, feature engineering, 5 models compared (Linear Regression to XGBoost), GridSearchCV, SHAP explanations, Streamlit app deployment.
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Unit 7: AI-Powered Analytics — LLMs, RAG & Vibe Coding 20 topics
LLMs for Data Analysts — Practical Overview
What are LLMs (GPT-4o, Claude, Gemini). How they work conceptually (tokens, context window). Why every data analyst needs to use them now.
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OpenAI API for Analysts — Getting Started
Get API key, install openai Python SDK. chat.completions.create(). System prompts for data analysis. Structured output with JSON mode.
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Chat with Your CSV — pandas-ai & LangChain
pandas-ai SmartDataframe for natural language DataFrame queries. LangChain CSV Agent. Demo: "What is the revenue trend by quarter?" → chart + insight.
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Chat with Your Database — Text-to-SQL Agents
LangChain SQL Agent connecting to MySQL/PostgreSQL. SQLDatabase wrapper. llm-based SQL generation. Vanna AI for natural language to SQL.
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RAG for Analytics — Q&A Over Documents
LangChain RAG pipeline: load PDF reports → split text → embed → FAISS vector store → RetrievalQA chain. Q&A over annual reports, research papers.
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AI-Automated EDA Reports
ydata-profiling for automatic EDA. SweetViz for comparison reports. D-Tale for interactive EDA. Feeding EDA results to Claude for auto-interpretation.
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AI-Generated Data Insights Narrative
Using OpenAI to auto-write insight paragraphs from data. Structured prompt: give summary stats → get executive summary. Building an auto-report generator.
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Automated Report Generation Pipeline
Python pipeline: pandas analysis → matplotlib charts → Jinja2 HTML template → WeasyPrint PDF → email with smtplib. Full end-to-end automated report.
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NLP for Analysts — Sentiment Analysis
Customer review analysis with TextBlob and VADER. Sentiment score visualization. Topic modeling with LDA basics. Word cloud from real data.
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NLP — Named Entity Recognition & Classification
spaCy for NER (extract company names, dates, locations from text). Zero-shot classification with Hugging Face transformers. Practical: classify support tickets.
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Forecasting with AI — Time-GPT & Chronos
Nixtla Time-GPT for zero-shot time series forecasting. Amazon Chronos model. Compare with Prophet. When foundation models beat classical methods.
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AI Dashboards — Building Smart Analytics Apps
Streamlit for data apps: st.dataframe, st.plotly_chart, st.selectbox. Adding an OpenAI chatbot to your dashboard. Deploy to Streamlit Cloud for free.
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Vibe Coding for Analysts — Build with Claude AI
Using Claude AI (claude.ai) to build complete data pipelines and dashboards. Prompt: "Build a pandas script that cleans this CSV and creates a Plotly dashboard." Review and refine.
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Microsoft Copilot for Data Work
Copilot in Excel for auto-analysis. Copilot in Power BI for DAX and reports. Copilot in Teams for meeting analysis. Bing Data Answers.
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Julius AI — No-Code AI Data Analysis
Julius AI for chat-with-data without coding. Upload CSV, ask questions, get charts. Compare with ChatGPT Advanced Data Analysis (Code Interpreter).
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ChatGPT Advanced Data Analysis (Code Interpreter)
Upload CSV to ChatGPT. Auto-EDA, auto-charts, auto-insights. Limitations. Combining with your own Python skills for best results.
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Google NotebookLM for Research Analytics
NotebookLM for Q&A over research papers and industry reports. Creating audio summaries. Building a research knowledge base. Analyst use case.
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Perplexity AI for Data Research
Perplexity for real-time market data research. Comparing with Google Search. Building research habit with AI. Citing sources properly.
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AI Ethics in Analytics — Bias, Hallucination, Trust
AI hallucination in data outputs. Verifying AI-generated insights. Bias in training data affecting model outputs. Responsible AI checklist for analysts.
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Practice Project — AI Analytics Pipeline
Build an automated analytics pipeline: Python + pandas + OpenAI API. Input: raw sales CSV → Output: cleaned data + 5 AI-generated insights + auto-charts + PDF report emailed.
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Unit 8: Real Projects, Portfolio & Career Readiness 18 topics
Project 1 — E-commerce Sales Analytics
Full project: 100K row e-commerce dataset. EDA, SQL queries, Python analysis, Power BI dashboard, AI-generated insights narrative. Business recommendations.
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Project 2 — HR Analytics & Attrition Prediction
HR dataset: employee attrition. EDA, feature engineering, logistic regression + XGBoost, SHAP explanations, Power BI people analytics dashboard.
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Project 3 — Financial Analytics Dashboard
Stock/financial data analysis with yfinance. Moving averages, RSI indicator. Monte Carlo simulation. Interactive Plotly dashboard. AI commentary.
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Project 4 — Customer Segmentation
RFM analysis (Recency, Frequency, Monetary). K-Means clustering. Segment profiling. Marketing recommendations per segment. Tableau visualization.
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Project 5 — Social Media Sentiment Dashboard
Twitter/Reddit data via API. TextBlob sentiment analysis. Real-time dashboard with Streamlit + Plotly. Word cloud. Brand monitoring use case.
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Building Your Data Portfolio on GitHub
GitHub repo structure for data projects. README with problem, approach, results. Jupyter notebook best practices (clear outputs, markdown cells). nbconvert to HTML.
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Kaggle for Career Development
Creating Kaggle profile. Participating in competitions (beginner to expert). Kaggle datasets for practice. Kaggle notebooks. Getting to Expert tier.
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Resume for Data Analyst Roles
ATS-optimized resume. Must-have sections: skills, projects, tools. Action verbs. Quantifying achievements. Tools to list: Python, SQL, Power BI, Excel, Tableau, pandas, sklearn.
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LinkedIn Profile Optimization for Data Roles
Headline formula. About section with keywords. Featured section for portfolio. Skills endorsements. Posting data insights to build authority.
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Interview Preparation — SQL Round
Top 30 SQL interview questions with solutions. Window functions Q&A. Query optimization questions. Live coding approach. Common mistakes.
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Interview Preparation — Python & Statistics Round
Python pandas questions. Statistical concepts tested in interviews. Probability problems. A/B testing questions. Case study approach.
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Interview Preparation — Case Study Round
Business case study framework: clarify → structure → analyze → recommend. Practice cases: pricing analytics, churn analysis, metric deep-dive.
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Interview Preparation — BI Tools Round
Power BI and Tableau practical questions. DAX formula writing under time pressure. Dashboard design questions. Live demo tips.
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Freelancing as a Data Analyst
Upwork and Fiverr for data projects. Pricing your services. Building a portfolio for freelancing. Typical client projects. Proposal writing.
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AI Tools for Job Search
Using Claude AI to tailor resume for each job. ChatGPT for interview prep. Jobscan for ATS optimization. LinkedIn Easy Apply strategy.
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Certifications That Matter in 2025
Google Data Analytics Certificate. Microsoft PL-300 (Power BI). Tableau Desktop Specialist. AWS Cloud Practitioner. Which ones are worth the time.
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Salary Negotiation for Data Roles
Market rates for Data Analyst in India by city and experience. How to negotiate. Counter-offer strategies. Total compensation understanding.
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Capstone Project — End-to-End Analytics
Choose a real business domain. Collect data (scraping/API/Kaggle). Full pipeline: clean → analyze → ML model → BI dashboard → AI insights → deploy Streamlit app → present.
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₹30,000
Enroll — ₹30,000
✨ 1-year access · Free updates included
30-day money back guarantee
Duration
90 Days
Format
Both Classes
Trainer
Lokesh Jammugani
Students
0 enrolled
Language
Telugu / English
Certificate
On completion
Placement
98% rate