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Data Science

Beginner → Intermediate

Data Science with Python

Master the complete data science workflow — from data collection and cleaning to analysis, visualization, and machine learning. Built around the three core libraries every data scientist uses daily: NumPy, Pandas, and Matplotlib.

12 Weeks 12 Modules Beginner → Intermediate 8–10 hrs/week

What you'll learn

  • Manipulate numerical data with NumPy
  • Clean, transform, and analyze data with Pandas
  • Create charts and visualizations with Matplotlib & Seaborn
  • Perform exploratory data analysis (EDA)
  • Build and evaluate basic ML models with Scikit-learn
  • Work with real-world datasets end-to-end
  • Present insights with interactive Plotly dashboards

Prerequisites

  • Basic Python knowledge (variables, loops, functions, lists)
  • No statistics or math background required — we cover what you need
  • No prior data science experience needed
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Tools & Tech:Python 3.12Jupyter NotebookNumPyPandasMatplotlibSeabornScikit-learnPlotlyGoogle ColabGit

Course Curriculum

01

Week 1 — Python for Data Science

1 week

Set up your data science environment and review Python essentials.

Jupyter Notebook & Google ColabPython Data Types ReviewList ComprehensionsLambda FunctionsFile I/O (CSV, JSON)pip & Virtual Environments
02

Week 2 — NumPy Fundamentals

1 week

Learn NumPy — the foundation of numerical computing in Python.

ndarray CreationArray Indexing & SlicingArray Operations & BroadcastingShape & ReshapeMathematical FunctionsRandom Number Generation
03

Week 3 — NumPy Advanced

1 week

Use NumPy for linear algebra, statistics, and performance-critical operations.

Linear Algebra (dot, matmul)Statistical Functions (mean, std, percentile)Boolean Masking & Fancy IndexingStacking & Splitting ArraysVectorization vs LoopsMemory Layout & Performance
04

Week 4 — Pandas Fundamentals

1 week

Learn Pandas — the go-to library for data manipulation and analysis.

Series & DataFrameReading CSV, Excel, JSONIndexing (loc, iloc)Filtering & Boolean IndexingAdding & Dropping ColumnsBasic Statistics (describe, value_counts)
05

Week 5 — Pandas Data Cleaning

1 week

Handle messy real-world data — missing values, duplicates, and inconsistencies.

Handling Missing Values (dropna, fillna)Duplicate Detection & RemovalData Type ConversionString Operations (str accessor)Replacing & Mapping ValuesOutlier Detection
06

Week 6 — Pandas Advanced Operations

1 week

Transform and aggregate data with groupby, merge, and pivot tables.

GroupBy & AggregationMerge, Join & ConcatPivot Tables & CrosstabApply & TransformWindow Functions (rolling, expanding)MultiIndex DataFrames
07

Week 7 — Time Series Analysis

1 week

Work with date/time data and analyze trends over time.

DatetimeIndexResampling & Frequency ConversionRolling AveragesLag FeaturesSeasonal DecompositionTime Zone Handling
08

Week 8 — Matplotlib & Seaborn

1 week

Create publication-quality charts and statistical visualizations.

Matplotlib Figure & AxesLine, Bar, Scatter, HistogramSubplots & LayoutsCustomization (colors, labels, annotations)Seaborn Statistical PlotsHeatmaps & Pair Plots
09

Week 9 — Exploratory Data Analysis (EDA)

1 week

Apply a systematic EDA process to understand any dataset.

EDA FrameworkUnivariate & Bivariate AnalysisCorrelation AnalysisDistribution AnalysisFeature RelationshipsEDA Report with Pandas Profiling
10

Week 10 — Interactive Visualizations with Plotly

1 week

Build interactive dashboards and charts with Plotly and Dash.

Plotly ExpressInteractive Charts (zoom, hover)Choropleth MapsAnimated ChartsDash BasicsDeploying a Dashboard
11

Week 11 — Machine Learning Fundamentals

1 week

Understand core ML concepts and build your first models with Scikit-learn.

Supervised vs Unsupervised LearningTrain/Test Split & Cross-validationLinear & Logistic RegressionDecision Trees & Random ForestModel Evaluation MetricsFeature Engineering Basics
12

Week 12 — End-to-End Project & Deployment

1 week

Complete a full data science project from raw data to deployed model.

Problem Definition & Data CollectionFull EDA & Feature EngineeringModel Selection & TuningModel Serialization (joblib/pickle)Flask API for Model ServingCapstone Project Presentation

What You'll Build

Sales Analysis Dashboard

Analyze retail sales data — trends, top products, regional performance.

PandasMatplotlibSeaborn

COVID-19 Data Tracker

Time series analysis and visualization of global COVID data.

PandasPlotlyTime Series

Customer Churn Prediction

Predict which customers will churn using classification models.

Scikit-learnEDAFeature Engineering

House Price Prediction

End-to-end regression project with EDA, feature engineering, and model deployment — capstone.

NumPyPandasScikit-learnFlask
🎓 After the Course

We don't stop when the course ends

Your journey continues — we actively support you in landing a job or launching your startup.

💼

Job Placement Assistance

We help you land your first developer role with resume building, portfolio reviews, mock interviews, and direct referrals to our hiring network.

  • Resume & LinkedIn profile review
  • Portfolio & GitHub profile optimization
  • Mock technical interviews
  • Referrals to partner companies & startups
  • Job search strategy & negotiation tips
  • Ongoing support until you get placed
🚀

Startup Launch Assistance

Have a startup idea? We help you validate it, build the MVP, and launch it — with hands-on technical mentorship from our team.

  • Idea validation & market research guidance
  • Technical architecture planning
  • Hands-on help building your MVP
  • Code reviews & mentorship sessions
  • App Store / Play Store launch support
  • Introductions to early users & investors

Every student who completes a course gets access to our placement & startup support program — at no extra cost.

Ready to start Data Science with Python?

Enroll now — build real projects, get placed, or launch your startup.

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