Data Science and Machine Learning: A Practical Guide in 2024

Dive Deep into Data Analysis, Visualization, and Predictive Modeling – Excel in the World of Data Science

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Description

Unlock the Power of Python for Data Science and Visualization



Welcome to a comprehensive Python programming course tailored by Selfcode Academy for data science and visualization enthusiasts. Whether you're a beginner or looking to expand your skill set, this course will equip you with the knowledge you need.


Master the Python Basics:

  • Start from scratch with Python fundamentals.

  • Learn about variables, data types, and the logic behind programming.

  • Explore conditional statements and loops.

  • Dive into essential data structures like lists, tuples, dictionaries, and sets.

  • Discover the world of functions, including powerful lambda functions.

  • Get familiar with Object-Oriented Programming (OOP) concepts.


Python's Role in Data Science:

  • Transition to data science seamlessly.

  • Manipulate dates and times using Python's datetime module.

  • Tackle complex text patterns with regular expressions (regex).

  • Harness the power of built-in Python functions.

  • Embrace NumPy for efficient numerical computing.

  • Master Pandas and its data structures, including Series and DataFrames.

  • Acquire data cleaning skills to handle missing values and outliers.

  • Excel at data manipulation with Pandas, including indexing, grouping, sorting, and merging.

  • Dive into data visualization with Matplotlib to create compelling graphs.


Advanced Data Science and Visualization:

  • Uncover insights through Exploratory Data Analysis (EDA) techniques.

  • Automate data analysis with Pandas Profiling, DABL, and Sweetviz.

  • Perfect your data cleaning and preprocessing techniques.

  • Craft captivating visualizations using Seaborn.

  • Create various plots, from lines and areas to scatter and violin plots with Plotly.

  • Take your data to the map with geographical visualizations.


Statistics and Hypothesis Testing:

  • Dive into descriptive statistics, including central tendency and dispersion.

  • Master inferential statistics, covering sampling, confidence intervals, and hypothesis testing.

  • Learn to conduct hypothesis tests using Python libraries.


Capstone Project:

  • Apply your skills to a real-world data science project.

  • Define a business problem and structure your analysis.

  • Summarize your findings in a comprehensive report.


Upon completing this course, you'll have a strong foundation in Python programming for data science and visualization. You'll possess the expertise to clean, analyze, and visualize data, empowering you to make data-driven decisions confidently.


Don't miss this opportunity to embark on your data science journey.

Enroll now and unleash the potential of Python for data exploration and visualization!


What You Will Learn!

  • Data Manipulation: Learn how to effectively manipulate and transform data using Python libraries such as Pandas, NumPy, and SciPy.
  • Data Analysis: Develop the ability to explore and analyze datasets using Python's powerful data visualization libraries like Matplotlib and Seaborn.
  • Gain hands-on experience in conducting EDA, including using tools like Pandas Profiling, DABL, and Sweetviz to analyze and visualize datasets.
  • Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions.
  • Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Apply knowledge and actionable insights from data across a broad range of application domains.

Who Should Attend!

  • This course is designed for individuals who are interested in learning and applying data science techniques using the Python programming language.
  • Aspiring Data Scientists: Individuals who want to pursue a career in data science and want to gain practical skills in using Python for data analysis, modeling, and visualization.
  • Python Programmers: Programmers who are already familiar with Python and want to expand their knowledge to the field of data science. This course will help them apply their programming skills to solve real-world data problems.
  • Data Analysts: Analysts who work with data and want to enhance their skills by incorporating Python into their data analysis workflows. This course will enable them to perform more advanced data manipulation, statistical analysis, and visualization using Python.