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Data Sciences Specialization Course

Data Sciences Specialization Course

The Data Sciences Specialization course is a comprehensive program designed to provide participants with the essential skills and knowledge required to excel in the field of data science. This course covers key concepts, tools, and techniques used in data analysis, machine learning, and statistical modeling. By the end of the course, participants will be equipped to handle real-world data science projects and make data-driven decisions

Key Learnings:

  • Data Science Fundamentals: Understand the core concepts and lifecycle of data science projects.
  • Data Wrangling: Learn techniques for cleaning, transforming, and preparing data for analysis.
  • Exploratory Data Analysis (EDA): Gain skills in visualizing and summarizing data to uncover patterns and insights.
  • Statistical Analysis: Master statistical methods for hypothesis testing, regression analysis, and probability.
  • Machine Learning: Learn to implement supervised and unsupervised learning algorithms.
  • Python for Data Science: Develop proficiency in using Python and its libraries for data analysis and machine learning.
  • Data Visualization: Acquire skills in visualizing data insights using tools like Matplotlib and Seaborn.
  • Capstone Project: Apply your knowledge to a real-world data science project.

Join this Course
Responsible HR & Marketing
Last Update 08/15/2024
Completion Time 20 hours
Members 1
Data Sciences Specialization Course Data Science Training Data Science
  • Module 1: Introduction to Data Science
    4Lessons · 4 hr
    • Overview of Data Science: Definition, Importance, and Applications
    • Data Science Project Lifecycle: From Data Collection to Model Deployment
    • Introduction to Python for Data Science: Jupyter Notebooks,
    • Data Collection and Data Types: Structured vs. Unstructured Data
  • Module 2: Data Wrangling and Exploration
    4Lessons · 5 hr
    • Data Cleaning Techniques: Handling Missing Data, Outliers, and Duplicate
    • Data Transformation: Scaling, Normalization, Encoding Categorical Data
    • Exploratory Data Analysis (EDA): Techniques and Tools
    • Data Visualization: Using Matplotlib and Seaborn for Data Exploration
  • Module 3: Data Visualization
    6Lessons · 5 hr
    • Introduction to Data Visualization
    • Importance of Data Visualization
    • Data Visualization Principles and Best Practices
    • Visualization Tools
    • Creating Line, Bar, Scatter, and Histogram Plots
    • Advanced Visualizations: Heatmaps, Pair Plots, and Geographical Plots
  • Module 4: Machine Learning Fundamentals
    5Lessons · 4 hr
    • Introduction to Machine Learning
    • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
    • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
    • Regression Algorithms: Linear Regression, Decision Trees
    • Classification Algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machines
  • Module 5: Advanced Machine Learning Techniques
    2Lessons · 2 hr
    • Unsupervised Learning, Clustering Algorithms: K-Means, Hierarchical Clustering Dimensionality Reduction: PCA, t-SNE
    • Introduction to Neural Networks,Basics of Neural Networks Introduction to Deep Learning and Neural Networks with TensorFlow/Kera