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Data Science with Python

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Data Science and Python

The journey of learning and understanding Data Science is very harsh. And when you are about to just make a career in data science it can be tougher. For establishing a secure career in data science, you need to learn a programming language. With data science, there are two types of programming languages: R and python. Which language is better which makes our career worth it?

The past candidates who have made their data science career have chosen python, which is helping them in all aspects of their professional life. But to establish a great career, some steps need to be considered so that you can go in the right direction.

  • Figure out what you need to learn - When you are selecting to make a career in data science, you must know which area you want to learn. Whether you want to learn with python or R or want to learn and understand statistics, linear algebra, calculus programming or more.
  • Get comfortable with python - As you know data science can be done with two programming languages which are R or python. Both are a great language but python is good for the industry whereas R is good for standing or academic basis. That's why you should choose Python and need to get comfortable with it by time.

Learn data analysis, manipulation, visualisation with pandas - When you are choosing data science with python, you should know the panda's library and its function to operate. It will provide a high data structure to your all data stored and perfect for tabular and columnar data.

Data Science with Python

Module 01 - Introduction to Data Science using Python

Module 02 - Python basic constructs

Module 03 - Maths for DS-Statistics & Probability

Module 04 - OOPs in Python (Self paced)

Module 05 - NumPy for mathematical computing

Module 06 - Scipy for scientific computing

Module 07 - Data manipulation

Module 08 - Data visualization with Matplotlib

Module 09 - Machine Learning using Python

Module 10 - Supervised learning

Module 11 - Unsupervised Learning

Module 12 - Python integration with Spark (Self paced)

Module 13 - Dimensionality Reduction

Module 14 - Time Series Forecasting

Data Science with Python

  • Descriptive statistics

  • Understanding distributions and plots, Understanding Operators

  • Univariate statistical plots and usage

  • Bivariate and multivariate statistics

  • Intro to python

  • Variables, operators, data types and strings in python

  • Tuples, list, dictionary and set in python

  • Python functions and classes

  • Intro to numpy array

  • Intro to linear regression

  • Relationship between independent variable and target variable

  • Coefficient of correlation

  • Linear regression assumptions

  • Introduction to logistic regression

  • Sigmoid curve and log loss function

  • Model cases of logistic regression

  • Variables and Data Types

  • Conditional Statement 

  • Looping Constructs

  • Functions

  • Data Structure

  • Lists

  • Dictionaries

  • Understanding Standard Libraries in Python 

  • Reading a CSV File in Python

  • Data Frames and basic operations with Data Frames 

  • Indexing a Dataframe 

  • Data Manipulation and Visualization 

  • Regular Expressions

  • Cheatsheet for Python

  • Evaluate

          1 Data Science with Python Training Overview

1.1 Objectives of the Course

1.2 Pre-requisites of the Course

1.3 Who can attend this course

2 Data Science with Python Course Content

2.1 Data Science with Python Course Introduction

2.2 Environment Set-Up

2.3 Jupyter Overview

2.4 Python Crash Course

2.5 Python for Data Analysis-NumPy

2.6 Python for Data Analysis-Pandas

2.7 Python for Data Analysis-Pandas Exercises

2.8 Python for Data Visualization-Matplotlib

2.9 Python for Data Visualization-Seaborn

2.10 Python for Data Visualization-Pandas Built-in Data Visualization

2.11 Python for Data Visualization-Plotly and Cufflinks

2.12 Python for Data Visualization-Geographical Plotting

2.13 Introduction to Machine Learning

2.14 Linear Regression

2.15 Logistic Regression

2.16 K Nearest Neighbours

2.17 Decision Trees and Random Forests

2.18 Support Vector Machines

2.19 K Means Clustering

2.20 Principal Component Analysis

2.21 Recommender Systems

2.22 Natural Language Processing

2.23 Big Data and Spark with Python

2.24 Neural Nests and Deep Learning


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10 months ago

clear concept platform thanku so muchhh iconitinc

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