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Data Science with Python

Data Science with Python is a comprehensive, hands-on course designed to teach participants how to analyze, visualize, and model data using Python. The course introduces core data science concepts and techniques, including data wrangling, statistical analysis, data visualization, and machine learning — all using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.

Participants will work on real-world datasets to gain practical experience and develop problem-solving skills through projects and exercises. By the end of the course, learners will be equipped to apply data science methodologies to extract insights, build predictive models, and support data-driven decision-making.

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🎯 Key Learning Objectives

By the end of this course, learners will be able to

Write Python code for data manipulation and analysis

Clean and transform raw data into usable formats

Perform exploratory data analysis (EDA) using visualizations

Apply statistical techniques for data interpretation

Build and evaluate machine learning models using Scikit-learn

Work on real-world data science projects from start to finish

🛠️ Course Features

100% practical and
project-based learning

Real-world
capstone projects

Code reviews and
mentor support

Resume and GitHub
portfolio development

Lifetime access to
course materials (if online)

Certification upon
successful completion

🧑‍💻 Who Should Take This Course

  • Aspiring Data Scientists and Data Analysts
  • Software Developers and Engineers transitioning into data roles
  • Business Analysts interested in upskilling in analytics
  • Students and researchers looking to apply data science in their field

🧰 Tools and Technologies Covered

Python
(core language)

Jupyter Notebook
(interactive coding environment)

NumPy and Pandas
(data manipulation)

Matplotlib and Seaborn
(data visualization)

Scikit-learn
(machine learning)

(Optional extensions: Plotly, Statsmodels, TensorFlow)

🧾 Data Science with Python

🔰 Module 1: Introduction to Data Science
  • What is Data Science?
  • Data Science workflow & lifecycle
  • Roles: Data Analyst vs Data Scientist
  • Overview of tools: Python, Jupyter, Pandas, Scikit-learn
  • Python basics: variables, data types, operators
  • Control structures: if-else, loops
  • Functions and lambda expressions
  • Data structures: lists, tuples, dictionaries, sets
  • Working with files (CSV, text)
  • Introduction to NumPy
  • Arrays, indexing, slicing, reshaping
  • Broadcasting, vectorized operations
  • Introduction to Pandas
  • Series and DataFrames
  • Importing/exporting data (CSV, Excel, SQL)
  • Data selection, filtering, sorting
  • Handling missing data, duplicates
  • Grouping and aggregation
  • Introduction to Matplotlib Line plots, bar charts, histograms, scatter plots
  • Advanced visualizations with Seaborn Box plots, violin plots, heatmaps, pair plots
  • Customizing plots (titles, labels, legends, colors)
  • Optional) Interactive visualizations using Plotly
  • Understanding data distribution
  • Detecting outliers and anomalies
  • Correlation and covariance analysis
  • Feature relationships and patterns
  • Building a data narrative
  • Descriptive statistics (mean, median, std dev)
  • Probability distributions
  • Sampling techniques
  • Central Limit Theorem
  • Hypothesis testing (t-test, chi-square)
  • p-values and confidence intervals
  • Supervised vs Unsupervised Learning
  • Train-test split & cross-validation
  • Overfitting and underfitting
  • Model evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC/AUC
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM) (optional)
  • K-Means Clustering
  • Hierarchical Clustering
  • PCA (Principal Component Analysis) for dimensionality reduction
  • Saving and loading models (Pickle, Joblib)
  • Basics of model deployment using Flask or Streamlit
  • Introduction to cloud platforms (Google Colab, AWS, etc.)
  • End-to-end project on a real-world dataset
  • Steps: Problem definition Data collection & cleaning Exploratory analysis Model building & evaluation Final presentation/report

📝 Assessment & Evaluation

  • Weekly exercises and quizzes
  • Mini projects for each major topic
  • Final capstone project with presentation

💼 Land job at your dream company

🪪 Course Certificate

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Official & Verified

Ideal for fast-paced learning with daily live sessions and hands-on projects

Easily Shareable

Suitable for working professionals or students balancing other commitments

Enhance Credebility

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Better Opportunities

Suitable for working professionals or students balancing other commitments

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