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Programming for Data Science with Python

The Programming for Data Science with Python course aims to equip participants with crucial programming abilities and tools necessary for effective data handling. 

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Programming for Data Science with Python course aims to equip participants with crucial programming abilities and tools necessary for effective data handling.

  2. Centered around Python, the leading language in data science, the curriculum addresses fundamental programming principles, data manipulation, data visualization, and basic machine learning methods.

  3. The course prioritizes practical experience through the use of real-world datasets and projects, enabling learners to address data challenges across different sectors.

  4. It is well-suited for both novices and professionals looking to shift into the field of data science.

CERTIFICATION:

  1. Individuals who successfully finish the course will be awarded a Certificate of Completion, showcasing their proficiency in utilizing Python for data analysis, visualization, and machine learning.

  2. This certification emphasizes their skills in programming and data science, preparing them for positions like data analyst, junior data scientist, or business intelligence analyst.

LEARNING OUTCOMES:

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

  1. participants will acquire essential skills in Python programming, encompassing fundamental concepts such as syntax, data types, and control structures.

  2. They will be equipped to write effective code for data handling and manipulation, utilizing popular Python libraries like NumPy, Pandas, Matplotlib, and Seaborn.

  3. Participants will learn to load, clean, and preprocess data from diverse sources, conduct exploratory data analysis to identify trends, and manage large datasets while optimizing performance.

  4. Additionally, they will develop the ability to create engaging visualizations using various libraries and gain a foundational understanding of machine learning, including the implementation of basic models with Scikit-learn and the evaluation of their performance through key metrics.

  5. Finally, learners will tackle real-world challenges through Python programming, culminating in comprehensive projects that integrate data cleaning, analysis, visualization, and introductory predictive modeling techniques.

  6. This course is tailored for beginners without prior programming knowledge, professionals aiming to shift into data science, and students looking to establish a solid groundwork in Python for data analysis.

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Course Curriculum

Introduction to Python for Data Science
  1. Why Python is essential for data science
  2. Setting up Python and Jupyter Notebook
  3. Overview of Python libraries: NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn
Python Basics
  1. Variables, data types, and operators
  2. Control structures: Conditional statements and loops
  3. Functions, modules, and libraries
Data Manipulation with Pandas
  1. Introduction to Pandas Series and DataFrames
  2. Data cleaning: Handling missing values and duplicates
  3. Data transformation: Sorting, filtering, and aggregations
Numerical Computing with NumPy
  1. Understanding NumPy arrays and their advantages
  2. Array creation, indexing, and slicing
  3. Performing mathematical operations and aggregations
Data Visualization
  1. Creating basic plots with Matplotlib
  2. Advanced visualizations with Seaborn (heatmaps, pair plots, etc.)
  3. Building interactive visualizations with Plotly
Introduction to Statistics and Probability
  1. Descriptive statistics: Mean, median, variance, and standard deviation
  2. Probability distributions and sampling
  3. Hypothesis testing basics
Introduction to Machine Learning
  1. Basics of supervised and unsupervised learning
  2. Building a simple linear regression model
  3. Evaluating model performance

Training Features

Interactive Learning

Hands-on coding exercises and quizzes to reinforce programming skills.

Real-World Datasets

Real-World Datasets Practice with datasets from industries like healthcare, finance, and e-commerce.

Project-Based Approach

Real-world projects to apply concepts and build a strong portfolio.

Comprehensive Resource Access

Downloadable notebooks, cheat sheets, and code templates.

Expert Mentorship

Guidance from industry professionals with experience in data science.

Certification

A recognized certificate of completion demonstrating data science programming skills with Python.

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