Data Science with Python

This course provides an introductory overview of data science, its significance in today's data-driven world, and its diverse applications. Students will gain a fundamental understanding of data collection, storage, and analysis.

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Data Science with Python

Course description

Create a dynamic career in the field of Data Science and Machine Learning with Besant’s Dual Master’s program Offered by Certified Expe...

Create a dynamic career in the field of Data Science and Machine Learning with Besant’s Dual Master’s program Offered by Certified Experts. Our curriculum covers all the concepts of Data Science enabling you to become an expert in these two complementary disciplines that organizations are seeking globally. 

What you’ll learn

These course descriptions provide a comprehensive overview of the curriculum at DSM Academy, ensuring that students receive a well-rounded education in data science.

Curriculum

Introduction to Data Science (6 lectures)
1. Selecting rows/observations

2. Rounding Number

3. Selecting columns/fields

4. Merging data

5. Data aggregation

6. Data munging techniques

Introduction to Python (5 lectures)
1. What is Python?

2. Why Python?

3. Installing Python

4. Python IDEs

5. Jupyter Notebook Overview

Python Basics (10 lectures)
1. Python Basic Data types

2. Lists

3. Slicing

4. IF statements

5. Loops

6. Dictionaries

7. Tuples

8. Functions

9. Array

10. Selection by position & Labels

Python Packages (4 lectures)
1. Pandas

2. Numpy

3. Sci-kit Learn

4. Mat-plot library

Importing Data (4 lectures)
1. Reading CSV files

2. Saving in Python data

3. Loading Python data objects

4. Writing data to CSV file

Manipulating Data (6 lectures)
1. Selecting rows/observations

2. Rounding Number

3. Selecting columns/fields

4. Merging data

5. Data aggregation

6. Data munging techniques

Statistics Basics (8 lectures)
1. Central Tendency

2. Probability Basics

3. Standard Deviation

4. Bias variance Tradeoff

5. Distance metrics

6. Outlier analysis

7. Missing Value treatment

8. Correlation

Error Metrics (2 lectures)
1. Classification

2. Regression

Frequently Asked Questions

Frequently Asked Questions

1. What is Data Science, and how is Python used in this field??

Explanation of data science as a discipline and Python's significance as a versatile programming language in data analysis and modeling.

2. How do I load and manipulate data in Python??

Demonstrations and explanations of data loading techniques and common data manipulation tasks using pandas.

3. What are the basics of data visualization in Python??

Overview of data visualization libraries in Python (Matplotlib, Seaborn) and techniques for creating informative plots and charts.

4. How can I perform statistical analysis with Python??

Guidance on using Python for basic and advanced statistical analysis, including hypothesis testing and regression.

5. How can I further my Python and Data Science skills after completing the basics??

Recommendations for advanced topics, resources, and projects to continue learning and improving data science skills.

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