Python for Data Science Certification

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With Python for Data Science certification gain expertise in Python programming like data operations, file operations etc. with various Python libraries.

Last updated on January 29, 2021 10:17 pm

Python for Data Science Certification

The Python for data science certification course enables you to learn data science concepts from scratch. This course is hosted by the Edureka learning platform, also this course is a gateway to your Data Science career. So, let’s see what is data science and why python is essential for Data Science.

What is Data Science?

Data-Analyst-vs-Data-Science

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. But how is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting.

“The goal is to turn data into information, and information into insight”.

–Carly Fiorin

As you can see from the above image, a Data Analyst usually explains what is going on by processing the history of the data. On the other hand, Data Scientist not only does exploratory analysis to discover insights from it but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

Why Learn Python?

Edurek Python for Data Science

Python is the programming language of choice for the daily tasks that data scientists tackle, and is one of the top and preferred data science tools used across industries. For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice.

It is also ideal for implementing algorithms, which is something that data scientists need to do often. There are also Python packages that are specifically tailored for certain functions, including pandas, NumPy, and  SciPy. Data scientists working on various machine learning tasks find that Python’s scikit-learn is a useful and valuable tool. Matplotlib, another one of Python’s packages, is also a perfect solution for data science projects that require graphics and other visuals. It is called ‘Pythonic’ when the code is written in a fluent and natural style. Thus python for data science has been a favorite programming language. Apart from that, Python is also known for other features that have captured the imaginations of the data science community.

About this course

This Python Course will also help you master important Python programming concepts such as data operations,  file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science.

What you will learn in Python for Data Science Course

  • Programmatically download and analyze data.
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding. and Learn data visualization.
  • Using I python notebooks, master the art of presenting step by step data analysis.
  • Gain insight into the ‘Roles’ played by a Machine Learning Engineer.
  • Describe Machine Learning and Work with real-time data.
  • Learn tools and techniques for predictive modeling.
  • Discuss and Validate Machine Learning algorithms and their implementation.
  • Explain Time Series and its related concepts.
  • Perform Text Mining and Sentimental analysis.
  • Gain expertise to handle business in the future, living the present.

Who this Course is for:

  • Programmers, Developers, Technical Leads, Architects
  • Developers aspiring to be a ‘Machine Learning Engineer’
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • ‘Python’ professionals who want to design automatic predictive models

Syllabus

1. Python for Data Science: Introduction to python programming

  • Overview of Python.
  • Different Applications where Python is used.
  • Values, Types, Variables, Conditional Statements, and Command-Line Arguments.
  • Companies using Python and Loops
  • Discuss Python Scripts on UNIX/Windows with Operands and Expressions.

2. Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions

  • Functions, Function Parameters, and Lambda Functions.
  • Global Variables, Global Variables, Variable Scope, and Returning Values
  • Object-Oriented Concepts.
  • Standard Libraries.
  • Modules Used in Python.
  • The Import Statements.
  • Module Search Path.
  • Errors and Exception Handling.

3. Python for Data Science: Data Manipulation

  • Basic Functionalities of a data object.
  • Merging of Data objects.
  • Concatenation of data objects and Types of Joins on data objects.
  • Exploring a Dataset and Analyzing a dataset.

4. Python for Data Science: Introduction to Machine Learning

  • Python Revision numpy, Pandas, scikit learn, and matplotlib.
  • What is Machine Learning?
  • Machine Learning Use-Cases, process flow, and categories.
  • Linear regression and Gradient descent.

5. Supervised Learning-1

  • What are Classification, its use cases, and decision Tree?
  • Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree and Confusion Matrix.
  • What is Random Forest?

6. Dimensionality Reduction

  • Introduction to Dimensionality, Dimensionality Reductio, PCA, Factor Analysis, and LDA.
  • Scaling dimensional model.

7. Supervised learning-2

  • What is Naïve Bayes and How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier.
  • What is Support Vector Machine and Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization Grid Search vs Random Search.
  • Implementation of Support Vector Machine for Classification.

8. Unsupervised Learning

  • What is Clustering & its Use Cases?
  • What is K-means Clustering and C-means Clustering?
  • How does the K-means algorithm work?
  • How to do optimal clustering
  • What is Hierarchical Clustering and How Hierarchical Clustering works?

9. Association Rules Mining and Recommendation Systems

  • What are Association Rules, Association Rule Parameters and Calculating Association Rule Parameters.
  • Recommendation Engines and How does Recommendation Engines work?
  • Collaborative Filtering and Content-Based Filtering.

10. Reinforcement Learning

  • What is Reinforcement Learning and Elements of Reinforcement Learning?
  • Exploration vs Exploitation dilemma.
  • Epsilon Greedy Algorithm.
  • Markov Decision Process (MDP).
  • Q values, Learning, V values, and α values.

11. Time Series Analysis

  • What is Time Series Analysis?
  • Importance of TSA and Components of TSA.
  • AR model, MA model, ARMA model, ARIMA model, and Stationarity.
  • ACF & PACF.

12. Model Selection and Boosting

  • What is Model Selection and need of Model Selection?
  • Cross-Validation.
  • What is Boosting and How Boosting Algorithms work?
  • Adaptive Boosting.

13. Sequences and File Operations

  • Python files I/O Functions and Numbers.
  • Strings, Tuples, Lists, Dictionaries, Sets related operations.

14. Python for Data Science: Introduction to NumPy, Pandas, and Matplotlib

  • NumPy – arrays and Operations on arrays.
  • Operations on arrays.
  • Indexing slicing and iterating.
  • Reading and writing arrays on files.
  • Pandas – data structures & index operations.
  • Reading and Writing data from Excel/CSV formats into Pandas.
  • matplotlib library.
  • Grids, axes, plots, Markers, colors, fonts, and styling.
  • Types of plots – bar graphs, pie charts, histograms, and Contour plots.

Prerequisites:

For the Python for Data Science course, basic understanding of Computer Programming Languages is a plus point. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be benificial. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the course.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

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  • Edureka
  • Online Course
  • Instructor-led
  • All levels
  • 1-4 Weeks
  • Paid Course (Paid certificate)
  • English
  • Data Science Data Science with 'Python' Machine learning
Expert Score
9.5
Learner rating
9.5
PROS: Batches are available in very convenient time slots. Excellent presentation of detailed data wrangling. Explanations are easy, clean and very easy to understand.
CONS: You should be familiar with programming fundamentals and Statistics Essentials One should have familiarity with basic concepts about Python programming.

Description

Python for Data Science Certification

The Python for data science certification course enables you to learn data science concepts from scratch. This course is hosted by the Edureka learning platform, also this course is a gateway to your Data Science career. So, let’s see what is data science and why python is essential for Data Science.

What is Data Science?

Data-Analyst-vs-Data-Science

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. But how is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting.

“The goal is to turn data into information, and information into insight”.

–Carly Fiorin

As you can see from the above image, a Data Analyst usually explains what is going on by processing the history of the data. On the other hand, Data Scientist not only does exploratory analysis to discover insights from it but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

Why Learn Python?

Edurek Python for Data Science

Python is the programming language of choice for the daily tasks that data scientists tackle, and is one of the top and preferred data science tools used across industries. For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice.

It is also ideal for implementing algorithms, which is something that data scientists need to do often. There are also Python packages that are specifically tailored for certain functions, including pandas, NumPy, and  SciPy. Data scientists working on various machine learning tasks find that Python’s scikit-learn is a useful and valuable tool. Matplotlib, another one of Python’s packages, is also a perfect solution for data science projects that require graphics and other visuals. It is called ‘Pythonic’ when the code is written in a fluent and natural style. Thus python for data science has been a favorite programming language. Apart from that, Python is also known for other features that have captured the imaginations of the data science community.

About this course

This Python Course will also help you master important Python programming concepts such as data operations,  file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science.

What you will learn in Python for Data Science Course

  • Programmatically download and analyze data.
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding. and Learn data visualization.
  • Using I python notebooks, master the art of presenting step by step data analysis.
  • Gain insight into the ‘Roles’ played by a Machine Learning Engineer.
  • Describe Machine Learning and Work with real-time data.
  • Learn tools and techniques for predictive modeling.
  • Discuss and Validate Machine Learning algorithms and their implementation.
  • Explain Time Series and its related concepts.
  • Perform Text Mining and Sentimental analysis.
  • Gain expertise to handle business in the future, living the present.

Who this Course is for:

  • Programmers, Developers, Technical Leads, Architects
  • Developers aspiring to be a ‘Machine Learning Engineer’
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • ‘Python’ professionals who want to design automatic predictive models

Syllabus

1. Python for Data Science: Introduction to python programming

  • Overview of Python.
  • Different Applications where Python is used.
  • Values, Types, Variables, Conditional Statements, and Command-Line Arguments.
  • Companies using Python and Loops
  • Discuss Python Scripts on UNIX/Windows with Operands and Expressions.

2. Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions

  • Functions, Function Parameters, and Lambda Functions.
  • Global Variables, Global Variables, Variable Scope, and Returning Values
  • Object-Oriented Concepts.
  • Standard Libraries.
  • Modules Used in Python.
  • The Import Statements.
  • Module Search Path.
  • Errors and Exception Handling.

3. Python for Data Science: Data Manipulation

  • Basic Functionalities of a data object.
  • Merging of Data objects.
  • Concatenation of data objects and Types of Joins on data objects.
  • Exploring a Dataset and Analyzing a dataset.

4. Python for Data Science: Introduction to Machine Learning

  • Python Revision numpy, Pandas, scikit learn, and matplotlib.
  • What is Machine Learning?
  • Machine Learning Use-Cases, process flow, and categories.
  • Linear regression and Gradient descent.

5. Supervised Learning-1

  • What are Classification, its use cases, and decision Tree?
  • Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree and Confusion Matrix.
  • What is Random Forest?

6. Dimensionality Reduction

  • Introduction to Dimensionality, Dimensionality Reductio, PCA, Factor Analysis, and LDA.
  • Scaling dimensional model.

7. Supervised learning-2

  • What is Naïve Bayes and How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier.
  • What is Support Vector Machine and Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization Grid Search vs Random Search.
  • Implementation of Support Vector Machine for Classification.

8. Unsupervised Learning

  • What is Clustering & its Use Cases?
  • What is K-means Clustering and C-means Clustering?
  • How does the K-means algorithm work?
  • How to do optimal clustering
  • What is Hierarchical Clustering and How Hierarchical Clustering works?

9. Association Rules Mining and Recommendation Systems

  • What are Association Rules, Association Rule Parameters and Calculating Association Rule Parameters.
  • Recommendation Engines and How does Recommendation Engines work?
  • Collaborative Filtering and Content-Based Filtering.

10. Reinforcement Learning

  • What is Reinforcement Learning and Elements of Reinforcement Learning?
  • Exploration vs Exploitation dilemma.
  • Epsilon Greedy Algorithm.
  • Markov Decision Process (MDP).
  • Q values, Learning, V values, and α values.

11. Time Series Analysis

  • What is Time Series Analysis?
  • Importance of TSA and Components of TSA.
  • AR model, MA model, ARMA model, ARIMA model, and Stationarity.
  • ACF & PACF.

12. Model Selection and Boosting

  • What is Model Selection and need of Model Selection?
  • Cross-Validation.
  • What is Boosting and How Boosting Algorithms work?
  • Adaptive Boosting.

13. Sequences and File Operations

  • Python files I/O Functions and Numbers.
  • Strings, Tuples, Lists, Dictionaries, Sets related operations.

14. Python for Data Science: Introduction to NumPy, Pandas, and Matplotlib

  • NumPy – arrays and Operations on arrays.
  • Operations on arrays.
  • Indexing slicing and iterating.
  • Reading and writing arrays on files.
  • Pandas – data structures & index operations.
  • Reading and Writing data from Excel/CSV formats into Pandas.
  • matplotlib library.
  • Grids, axes, plots, Markers, colors, fonts, and styling.
  • Types of plots – bar graphs, pie charts, histograms, and Contour plots.

Prerequisites:

For the Python for Data Science course, basic understanding of Computer Programming Languages is a plus point. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be benificial. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the course.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • Edureka
  • Online Course
  • Instructor-led
  • All levels
  • 1-4 Weeks
  • Paid Course (Paid certificate)
  • English
  • Data Science Data Science with 'Python' Machine learning

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Python for Data Science Certification
Python for Data Science Certification

$349.00

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