DATA SCIENCE OVERVIEW

Data Science combines statistics, scientific methods, Artificial Intelligence and Data Analysis to extract the value of data. To provide meaningful insights, data scientists employ a variety of skills to analyse data acquired from the web, smartphones, customers, sensors, and other sources. The process of cleaning, collecting, and changing data in order to undertake advanced data analysis is known as data cleansing. As a result of the vast amount of data being generated and the breakthroughs in the field of analytics, it has become a requirement for businesses. Companies from a variety of industries, including finance, marketing, retail, information technology, and banking, are working to develop the most up-to-date information. They’re all on the lookout for scientists who can provide them with knowledge. As a result, data scientists are in great demand all around the world, and it may be a good option for someone to take as a profession.

Duration: 60 hours

Course Content:

1. Introduction to Data Science

• Meaning?
• Need
• Applications
• Libraries used

2. NumPy Library

• Introduction to NumPy Library
• Creating NumPy Arrays
• Numpy Array Indexing
• Array Slicing
• Numpy Array Operations

3. Pandas Library

• Introduction to Pandas Library
• Creating Series
• Accessing Series
• Creating DataFrames
• Accessing DataFrames
• Adding and Removing Rows in DataFrame
• Adding and Removing Columns in DataFrame
• Working with Missing Values in DataFrame
• Iterating elements of DataFrame
• Descriptive statistics in DataFrame
• Creating DataFrame from CSV file
• Writing DataFrame data into a CSV file

4. Matplotlib Library

• Introduction to Matplotlib Library
• Creating a Line Plot
• Formatting a Line Plot
• Creating a Bar Plot
• Formatting a Bar Plot
• Creating a Scatter Plot
• Formatting a Scatter Plot
• Creating a Histogram Plot
• Formatting a Histogram Plot
• Creating a Box Plot
• Formatting a Box Plot
• Creating a Pie Plot
• Formatting a Pie Plot

6. Decision Tree Algorithm

• Decision Tree Algorithm Concept
• Decision Tree Algorithm Program Implementation

5. Seaborn Library

• Introduction to Seaborn Library
• Distribution Plots
• Categorical Plots
• Matrix Plots
• Grids
• Regression Plots
• Styling and Coloring Plots

8. Support Vector Machine Algorithm

• Support Vector Algorithm Concept
• Support Vector Algorithm Program Implementation

6. Project Work

• Covering all the Concepts.
• Project Works

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