Here’s an overview of what you might expect to learn in a Data Science 101 .

1. What is Data Science?:

• Introduction to the field of data science and its significance in various industries.
• Understanding the role of data scientists and data analysts.

2. Data Types and Sources:

• Different types of data: structured, semi-structured, and unstructured data.
• Sources of data, including databases, spreadsheets, web data, and more.

3. Data Collection and Preparation:

• Techniques for collecting, cleaning, and preprocessing data.
• Dealing with missing data and outliers.

4. Data Exploration and Visualization:

• Exploratory Data Analysis (EDA) techniques to understand data distributions and relationships.
• Creating visualizations using tools like Matplotlib, Seaborn, or ggplot2.

5. Basic Statistics:

• Key statistical concepts such as mean, median, variance, standard deviation, and probability.
• Descriptive statistics and summary measures.

6. Data Analysis:

• Introduction to statistical analysis and hypothesis testing.
• Using statistical tests to make data-driven decisions.

7. Introduction to Machine Learning:

• Overview of machine learning concepts and terminology.
• Understanding supervised and unsupervised learning.

8. Machine Learning Algorithms:

• Basic understanding of common machine learning algorithms, such as linear regression, logistic regression, decision trees, and clustering.

9. Model Evaluation:

• How to assess the performance of machine learning models using metrics like accuracy, precision, recall, and F1-score.

10. Data Visualization Tools: – Introduction to data visualization tools like Tableau, Power BI, or open-source alternatives. – Creating charts, graphs, and dashboards.

11. Case Studies and Projects: – Hands-on projects and case studies to apply what you’ve learned. – Solving real-world data analysis problems.

12. Ethics and Data Privacy: – Considerations related to data ethics, privacy, and responsible data handling.

13. Tools and Programming Languages: – Introduction to programming languages commonly used in data science, such as Python and R. – Basics of using Jupyter Notebooks for data analysis.

14. Resources and Further Learning: – Guidance on additional resources, books, courses, and communities for further learning in data science.