Educate Nxt Web Development Python

Python for Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

  Advanced     Last Updated At 14/2022
  English     English [Auto]

Key Learning

Master Machine Learning on Python & R

Have a great intuition of many Machine Learning models

Make accurate predictions

Make powerful analysis

Make robust Machine Learning models

Create strong added value to your business

Use Machine Learning for personal purpose

Handle specific topics like Reinforcement Learning, NLP and Deep Learning

Handle advanced techniques like Dimensionality Reduction

Know which Machine Learning model to choose for each type of problem

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

7 sections • 80 lessons • 40 Hours total length
Understanding the reason of Python’s popularity and why it is important to learn
Basics of Python: Operators, Data Types, Control Structures
Functions in Python and scoping
What is data science? How is data science different from BI and Reporting?
What is difference between AI, Data Science, Machine Learning, Deep Learning
Job Land scape and Preparation Time
Who are data scientists? What skillsets are required?
What is day to day job of Data Scientist? What kind of projects they work on?
End to End Data Science Project Life Cycle
Data Science Job roles – functions, pay across domains, experience"
Data types - Numerical and Categorical data
Different type of Numerical and Categorical data
Descriptive Statistics Plots/charts
Central Measures of Tendency
Central Measures of Dispersion
What is Descriptive & Inferential statistics?
What is Numpy
Installation of Numpy library
Numpy Features
Numpy arrays
Single and Multidimensional arrays
Indexing
Slicing
Element by Element operations in Numpy
Arithmetic operations in Numpy
How to calculate mean, median, standard deviation using Numpy
Numpy matrix
Vector multiplication
Resizing, Reshaping and Transpose functions
Inverse function
Boolean filtering
Querying using where() function in numpy
Broadcasting
Numpy random module
Pandas Introduction
Installation of Pandas library
Pandas Data Structures
Basics of Series & Dataframes
Data types in Pandas
Data collection/loading using Pandas
Creation, Indexing and Slicing in Data Frames
Loc and iLoc functions usage and difference
Updating particular value in DataFrame
Identifying missing and duplicate values
Detecting outliers
Cleaning data
Sort, Merge and Join operations in Pandas
groupby function in pandas
apply function in pandas
Creating Pivot table and its operations in pandas
Exploratory Data Analysis(EDA) using Pandas
Data correlation
Outlier Identification using plots
Importance of Data Visualization
Data Visualization libraries in Python
Matplotlib Introduction
Installation of Matplotlib library
Basic structure of Matplotlib graphs
Line plot
Configuring axis and labels
Configuring ticks and legend
Setting colors, markers, styles and figure size
Applications of Line Plot
Bar chart
Applications of Bar plot
Histogram chart
Applications of Histogram plot
Scatter plot
Applications of Scatter chart
Subplots
Heat maps in matplotlib
3D plotting basics
What is Web scraping?
Importance of web scraping in real life applications
Why web scraping important in Data science world
Introduction to Beautiful Soup
Installation of Beautiful Soup library
How Beautiful soup helps in web scraping HTML pages
Type of objects in Beautiful soup
Navigating and Searching HTML pages

This Data Science with Python training course teaches engineers, data scientists, statisticians, and other quantitative professionals the Python programming skills they need to analyze and chart data.


Data Science has been among the top paying jobs for the past several years. The rise of big data and use of analytics to fuel business growth has made it among the most in-demand jobs in enterprises. Start your path to becoming a Data Scientist, using the power of Python. Analyze data, create beautiful visualizations, and use powerful machine learning algorithms to convert your data into meaningful statistics that can help organizations achieve business outcomes.


This interactive and comprehensive course is a great place for you to get started on Python programming language and its use in Data Science. This Data Science with Python course aims at helping you understand the core concepts of Data science including exploratory data science, statistics, hypothesis testing, regression classification modeling techniques, data visualization and machine learning algorithms. Coaching from experts and plenty of hands-on exercises will ensure that you are industry ready by the end of this course.

Objectives: 

  • Upon successful completion of this training learners will be able to: 
  • Understand the importance of Data science and its demand in the current industry.
  • Quick Refresher on Python if you have a Python background and want to revise once.
  • Learn foundational statistics required to understand Data science concepts.
  • Discover how to programmatically download and analyze big data sets.
  • Explore techniques on how to deal with different types of data such as numerical, categorical. 
  • Discover various measures of cleaning data, identifying outliers and fixing them.
  • Understand how python libraries are useful in scientific analysis. 
  • Learn how to visualize the data analyzed to provide meaningful insights.
  • Learn how to use numpy, pandas and matplotlib in different stages of data science life cycle process. 

 

Who can take this course? 

  • Anybody who has good programming knowledge or experience in python can take this course to get into the data science world. The course is intended for Students, Freshers or learners with intermediate programming knowledge such as Developers, Program Analysts, Tech Leads, Architects, Project Managers, Technical Program managers and Product Managers. 
  • Career Opportunities: 
  • Data Analyst 
  • Data Scientist 
  • Data Engineer 
  • Business Analyst 
  • Data and Analytics Manager 
  • Data Architect 
  • Developer

Prerequisites
Learners should have prior programming experience and an understanding of basic statistics.



Started From

₹ 20000

This course includes:

Python for Data Science

Buy Now
Educate Nxt Web Development Python

Python for Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

  Advanced     Last Updated At 14/2022
  English     English [Auto]


Request a Demo



Started From

₹ 20000

This course includes:

Python for Data Science

Buy Now

Key Learning

Master Machine Learning on Python & R

Have a great intuition of many Machine Learning models

Make accurate predictions

Make powerful analysis

Make robust Machine Learning models

Create strong added value to your business

Use Machine Learning for personal purpose

Handle specific topics like Reinforcement Learning, NLP and Deep Learning

Handle advanced techniques like Dimensionality Reduction

Know which Machine Learning model to choose for each type of problem

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

7 sections • 80 lessons • 40 Hours total length
Understanding the reason of Python’s popularity and why it is important to learn
Basics of Python: Operators, Data Types, Control Structures
Functions in Python and scoping
What is data science? How is data science different from BI and Reporting?
What is difference between AI, Data Science, Machine Learning, Deep Learning
Job Land scape and Preparation Time
Who are data scientists? What skillsets are required?
What is day to day job of Data Scientist? What kind of projects they work on?
End to End Data Science Project Life Cycle
Data Science Job roles – functions, pay across domains, experience"
Data types - Numerical and Categorical data
Different type of Numerical and Categorical data
Descriptive Statistics Plots/charts
Central Measures of Tendency
Central Measures of Dispersion
What is Descriptive & Inferential statistics?
What is Numpy
Installation of Numpy library
Numpy Features
Numpy arrays
Single and Multidimensional arrays
Indexing
Slicing
Element by Element operations in Numpy
Arithmetic operations in Numpy
How to calculate mean, median, standard deviation using Numpy
Numpy matrix
Vector multiplication
Resizing, Reshaping and Transpose functions
Inverse function
Boolean filtering
Querying using where() function in numpy
Broadcasting
Numpy random module
Pandas Introduction
Installation of Pandas library
Pandas Data Structures
Basics of Series & Dataframes
Data types in Pandas
Data collection/loading using Pandas
Creation, Indexing and Slicing in Data Frames
Loc and iLoc functions usage and difference
Updating particular value in DataFrame
Identifying missing and duplicate values
Detecting outliers
Cleaning data
Sort, Merge and Join operations in Pandas
groupby function in pandas
apply function in pandas
Creating Pivot table and its operations in pandas
Exploratory Data Analysis(EDA) using Pandas
Data correlation
Outlier Identification using plots
Importance of Data Visualization
Data Visualization libraries in Python
Matplotlib Introduction
Installation of Matplotlib library
Basic structure of Matplotlib graphs
Line plot
Configuring axis and labels
Configuring ticks and legend
Setting colors, markers, styles and figure size
Applications of Line Plot
Bar chart
Applications of Bar plot
Histogram chart
Applications of Histogram plot
Scatter plot
Applications of Scatter chart
Subplots
Heat maps in matplotlib
3D plotting basics
What is Web scraping?
Importance of web scraping in real life applications
Why web scraping important in Data science world
Introduction to Beautiful Soup
Installation of Beautiful Soup library
How Beautiful soup helps in web scraping HTML pages
Type of objects in Beautiful soup
Navigating and Searching HTML pages

This Data Science with Python training course teaches engineers, data scientists, statisticians, and other quantitative professionals the Python programming skills they need to analyze and chart data.


Data Science has been among the top paying jobs for the past several years. The rise of big data and use of analytics to fuel business growth has made it among the most in-demand jobs in enterprises. Start your path to becoming a Data Scientist, using the power of Python. Analyze data, create beautiful visualizations, and use powerful machine learning algorithms to convert your data into meaningful statistics that can help organizations achieve business outcomes.


This interactive and comprehensive course is a great place for you to get started on Python programming language and its use in Data Science. This Data Science with Python course aims at helping you understand the core concepts of Data science including exploratory data science, statistics, hypothesis testing, regression classification modeling techniques, data visualization and machine learning algorithms. Coaching from experts and plenty of hands-on exercises will ensure that you are industry ready by the end of this course.

Objectives: 

  • Upon successful completion of this training learners will be able to: 
  • Understand the importance of Data science and its demand in the current industry.
  • Quick Refresher on Python if you have a Python background and want to revise once.
  • Learn foundational statistics required to understand Data science concepts.
  • Discover how to programmatically download and analyze big data sets.
  • Explore techniques on how to deal with different types of data such as numerical, categorical. 
  • Discover various measures of cleaning data, identifying outliers and fixing them.
  • Understand how python libraries are useful in scientific analysis. 
  • Learn how to visualize the data analyzed to provide meaningful insights.
  • Learn how to use numpy, pandas and matplotlib in different stages of data science life cycle process. 

 

Who can take this course? 

  • Anybody who has good programming knowledge or experience in python can take this course to get into the data science world. The course is intended for Students, Freshers or learners with intermediate programming knowledge such as Developers, Program Analysts, Tech Leads, Architects, Project Managers, Technical Program managers and Product Managers. 
  • Career Opportunities: 
  • Data Analyst 
  • Data Scientist 
  • Data Engineer 
  • Business Analyst 
  • Data and Analytics Manager 
  • Data Architect 
  • Developer

Prerequisites
Learners should have prior programming experience and an understanding of basic statistics.


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