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Introduction to Data Science

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Introduction to Data Science course aims to equip participants with essential knowledge and practical abilities in data science.

  2. It emphasizes the comprehension of data, the exploration of analytics, and the application of various techniques to derive actionable insights. Key topics include data collection, cleaning, visualization, statistical analysis, and machine learning, with a focus on hands-on learning through projects and case studies.

  3. This course is well-suited for novices lacking prior experience in data science, although a fundamental understanding of mathematics and programming is advantageous.

CERTIFICATION:

  1. Upon finishing the course, participants will be awarded a Certificate of Completion, which confirms their expertise in data science.

  2. This certification signifies competence in essential data science tools and methodologies, enhancing opportunities for career progression or additional academic pursuits in the discipline.

LEARNING OUTCOMES:

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

  1. participants will be equipped to:

    Comprehend Data Science Fundamentals: Acquire a solid understanding of essential concepts, terminology, and processes integral to data science workflows.

  2. Handle Data: Gather, clean, and prepare both structured and unstructured data for analysis.
    Conduct Exploratory Data Analysis (EDA): Employ statistical techniques and visualization tools to identify patterns and derive insights.
    Utilize Tools Effectively:

  3. Develop expertise in data science tools and programming languages such as Python, R, SQL, along with libraries like Pandas, NumPy, and Matplotlib.
    Create Predictive

  4. Models: Implement machine learning methods to design and assess predictive models.
    Address Practical

  5. Challenges: Engage in hands-on projects that tackle real-world business or scientific issues.

  6. Present Findings: Generate data-driven reports and dashboards to communicate insights effectively to stakeholders.

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Course Curriculum

What is Data Science?
  1. Definition and importance of data science
  2. Key components: Data collection, processing, analysis, and interpretation
  3. Roles and responsibilities of a data scientist
Tools and Technologies for Data Science
  1. Overview of data science tools: Python, R, SQL
  2. Introduction to Jupyter Notebook, Anaconda, and IDEs
  3. Libraries for data manipulation and visualization: Pandas, Matplotlib, and Seaborn
Data Collection and Cleaning
  1. Sources of data: Structured, unstructured, and semi-structured data
  2. Web scraping basics and APIs for data extraction
  3. Data cleaning techniques: Handling missing values, duplicates, and outliers
Exploratory Data Analysis (EDA)
  1. Importance of EDA in the data science workflow
  2. Statistical summaries and visualizations
  3. Identifying patterns, trends, and anomalies
Introduction to Machine Learning
  1. Basics of supervised and unsupervised learning
  2. Key algorithms: Linear regression, decision trees, clustering
  3. Steps to build a machine learning model
Data Visualization
  1. Principles of effective data visualization
  2. Creating charts and graphs with Matplotlib and Seaborn
  3. Introduction to advanced tools like Tableau and Power BI
Data Science Workflow
  1. Defining the problem and setting goals
  2. Collecting and understanding data
  3. Performing analysis and generating insights
  4. Presenting findings to stakeholders
Real-World Applications
  1. Case study: Customer segmentation using clustering
  2. Case study: Predicting sales using regression analysis
  3. Capstone project: End-to-end data science project

Training Features

Comprehensive Beginner-Friendly Content

Designed for individuals new to data science, with easy-to-follow explanations and examples.

Interactive Coding Exercises

Hands-on practice using Python and real-world datasets to reinforce learning.

Case Studies and Projects

Work on practical, industry-relevant projects to apply data science concepts.

Visualization-Focused Learning

Emphasis on creating compelling visualizations to effectively communicate data insights.

Mentor Support and Live Q&A

Access to experienced data science professionals for personalized guidance.

Certification

Receive a certificate validating your understanding of foundational data science concepts.

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