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Data Science with Python

Learn data manipulation, analysis, and visualization using Python libraries such as pandas, NumPy, and Matplotlib.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Acquire fundamental skills in Data Science with Python, a leading programming language for data analysis and machine learning.

  2. This course covers the entire data science workflow, including data gathering, preprocessing, visualization, and modeling.

  3. Utilize Python’s robust libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn for effective data insights.

  4. Enhance your ability to make informed, data-driven decisions through practical applications.

  5. Develop predictive models to solve real-world problems using advanced data techniques.

CERTIFICATION:

  1. Earn a Certified Data Scientist with Python credential, demonstrating your ability to analyze and model data effectively.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp fundamental concepts of data science and its practical uses.

  2. Clean, preprocess, and analyze data utilizing Pandas and NumPy.

  3. Create visual representations of data with Matplotlib and Seaborn for meaningful insights.

  4. Conduct statistical analyses and perform hypothesis testing.

  5. Develop and assess machine learning models with Scikit-learn, applying them to real-world datasets for business solutions.

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Course Curriculum

Introduction to Data Science and Python
  1. What is Data Science?
    • Overview of data science concepts, lifecycle, and importance.
    • Applications of data science in industries like finance, healthcare, and e-commerce.
  2. Getting Started with Python
    • Introduction to Python programming.
    • Setting up Python environments with Anaconda or Jupyter Notebook.
Python Basics for Data Science
  1. Python Essentials
    • Data types, variables, loops, and conditionals.
    • Functions, modules, and packages.
  2. Data Structures
    • Lists, tuples, dictionaries, and sets.
    • Working with complex data structures for analysis.
Data Manipulation
  1. Introduction to Pandas
    • DataFrames and Series.
    • Importing and exporting data (CSV, Excel, JSON, etc.).
  2. Data Cleaning
    • Handling missing values, duplicates, and outliers.
    • Data transformations: Renaming, reshaping, and merging datasets.
Data Visualization
  1. Introduction to Visualization
    • Importance of data visualization in data science.
  2. Visualization Tools
    • Matplotlib: Line plots, bar plots, scatter plots.
    • Seaborn: Heatmaps, pair plots, and categorical plots.
    • Advanced visualizations with Plotly.
Exploratory Data Analysis (EDA)
  1. Understanding EDA
    • Descriptive statistics and data distribution.
    • Identifying patterns, correlations, and anomalies.
  2. Tools for EDA
    • Pandas Profiling.
    • Creating dashboards using Python libraries.
Statistical Analysis
  1. Basics of Statistics
    • Mean, median, mode, variance, and standard deviation.
  2. Hypothesis Testing
    • T-tests, Chi-square tests, and ANOVA.
  3. Probability
    • Distributions: Normal, Poisson, and Binomial.
    • Bayes Theorem and its applications.
Machine Learning Basics
  1. Supervised Learning
    • Regression: Linear and Logistic.
    • Classification: Decision Trees, Random Forests, SVM.
  2. Unsupervised Learning
    • Clustering: K-Means and Hierarchical Clustering.
    • Dimensionality Reduction: PCA.
  3. Model Evaluation
    • Metrics: Accuracy, Precision, Recall, and F1 score.
    • Cross-validation techniques.
Data Wrangling with NumPy
  1. Introduction to NumPy
    • Arrays, indexing, and slicing.
    • Mathematical operations on arrays.
  2. NumPy for Scientific Computing
    • Working with multidimensional data.
    • Linear algebra operations.
Capstone Project
  1. End-to-End Data Science Project
    • Collect, clean, and analyze a real-world dataset.
    • Build predictive models and visualize insights.
    • Create a detailed report and deploy the project.

Training Features

Hands-On Learning

Work on real-world datasets from Kaggle and UCI Machine Learning Repository.

Comprehensive Coverage

Covers everything from Python basics to advanced data science techniques.

Practical Applications

Focus on industry use cases such as fraud detection, customer segmentation, and stock market analysis.

Interactive Learning

Live coding exercises, quizzes, and group projects.

Career Support

Resume-building tips, interview preparation, and portfolio creation.

Certification

Earn a recognized certificate of completion to showcase your skills.

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