Python Certification Training for Data Science

$589

$529

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Categories
Data Science

Course Curriculum

Learning Objective: You will get to know about the basics of python and a brief idea about them.


Topics:

  •  Brief Overview of Python
  •  Companies utilizing Python
  •  Various applications of Python
  •  Scripts of Python being used on Windows/UNIX
  •  Variables, Types, Values
  •  Expressions and Operands
  •  Loops and Conditional Statements in Python
  •  Arguments of Command Line

Hands-On

  • Writing the “Hello World” code
  • Demonstrating the use of loops and conditional statements
  • Use of variables

Learning Objective: Usage of different types of related operations and sequence structures. Exploring the various ways to open, read, and write the files.

Topics:
  • I/O Functions of Python Files
  • Set operations
  • Dictionaries and its related operations
  • Lists and its related operations
  • Tuples and its related operations
  • Strings and its related operations
  • Numbers
Hands-On:
  • Properties and related operations of Tuples being compared with a list
  • Properties and related operations of Set
  • Properties and related operations of Dictionary
  • Properties and related operations of List
Learning Objective: In this module, you will learn the creation of python scripts, addressing errors or exceptions in the code, and the extraction/filtering content using regex.

Topics:
  • Handle multiple exceptions
  • Exception and error handling
  • Ways of package installation
  • Module search path
  • Implement import statements
  • Python modules
  • Standard libraries of python
  • Different object-oriented concepts
  • Lambda functions
  • Returning scope and values of a variable
  • Use of global variables
  • Functions and function parameters
Hands-On:
  • Modules and import options of packages
  • Types of issues and remediation of exceptions and errors
  • Sorting dictionaries and sequences and limitations of sorting
  • Syntax, options and features of lambda along with its features comparison
  • Syntax, return values, keyword arguments, and arguments of functions
Learning Objective: In this module, you will learn about various libraries in python. You will also learn about the basics of statistics, details of data visualization, and different types of probability distributions and measures.

Topics:
  • Contour plots
  • Types of plots like histograms, pie charts, and bar graphs
  • Styling, fonts, colours, and markers
  • Plots, axes, and grid
  • matplotlib library
  • Reading as well as writing the data into Pandas from the CSV/Excel formats
  • Index operations and data structures in Pandas
  • Reading and writing arrays on the files
  • Operations on arrays and NumPy – arrays
Hands-On:
  • Usage of the styling of a plot, bar graph, pie chart, and histogram
  • Creating NumPy arrays
  • Importing and exporting data in Pandas library
  • Creation of dataframes and series in Pandas library
  • Creation of NumPy arrays
  • Performing different operations on NumPy arrays
Learning Objective: In this module, you will understand the concept of Data Manipulation.

Topics:
  • Fundamental functionalities of any data object
  • Data objects merging
  • Data objects concatenation
  • Different types of joins on data objects
  • Exploring and analyzing a dataset
Hands-On: 
  • Joining
  • Merging
  • Concatenation
  • Aggregation
  • GroupBy operations
  • Different functions of Pandas – itertuples(), iterrows(), iteritems(), std(), sum(), tail(), head(), values(), axes(), Ndim()
Learning Objective: In this module, you will discover the concept of Machine learning along with its types. 

Topics:
  • Gradient descent
  • Linear regression
  • Categories of Machine Learning
  • Process Flow of Machine Learning
  • Use-Cases of Machine Learning
  • Basics of Machine Learning
  • Revision of Python
Hands-On: 
  • Boston Dataset and Linear Regression
Learning Objective: In this module, you will get to know about different techniques of Supervised learning along with its implementation.

Topics:
  • Random Forest
  • Confusion Matrix
  • Creation of Perfect Decision Tree
  • Decision Tree Induction Algorithm
  • Decision Tree
  • Classifications and its use-cases
  • Hands-On:
  • Random forest
  • Decision tree
  • Logistic regression implementation
Learning Objective: In this module, you will get to know about the impact of dimensions on data, develop an LDA model, and factor analysis using PCA.

Topics:
  • Introduction to dimensionality
  • LDA
  • Scaling dimensional model
  • Factor analysis
  • PCA
  • Dimensionality reduction basics
Learning Objective: In this module, you will learn about different techniques of Supervised Learning and its implementation.

Topics:
  • Naïve Bayes
  • Working of Naïve Bayes
  • Implementation of naïve bayes classifier
  • Support vector machine
  • Working of support vector machine
  • Hyperparameter optimization
  • Random search vs Grid search
  • Implementation of support vector machine
Hands-On:
  • SVM and implementation of naïve bayes
Learning Objective: In this module, you will learn about different types of clustering for analyzing the data and unsupervised learning.

Topics:
  • Clustering and its use-cases
  • K-means clustering
  • Working of K-means algorithm
  • Optimal clustering
  • C-means clustering
  • Hierarchical clustering
  • Working of hierarchical clustering
Hands-On:
  • Implementation of hierarchical clustering
  • Implementation of K-means clustering
Learning Objective: In this module, you will learn about the Apriori algorithm and association rules.
Topics:
  • Association rules
  • Parameters of association rules
  • Calculation of association rule parameters
  • Recommendation engines
  • Working of recommendation engines
  • Content-based filtering
  • Collaborative filtering
Hands-On:
  • Market basket analysis
  • Apriori algorithm
Learning Objective: In this module, you will learn about developing a smart learning algorithm that can make the learning process more accurate and define an optimal solution based on the agent-environment interaction.
Topics:
  • Reinforcement learning
  • Elements of reinforcement learning
  • The dilemma of exploitation vs exploration
  • Markov decision process (MDP)
  • Q-learning
  • Q values and V values
  • ? values
  • Epsilon greedy algorithm
Hands-On:
  • Optimal action set up
  • Implementation of Q learning
  • Calculation of optimal quantities
  • Calculating and discounted reward
Learning Objective: In this module, you will learn about different models for time series modeling in such a way that you can analyze a data-dependent on real-time for forecasting. 
Topics:
  • Time series analysis (TSA)
  • Components and importance of TSA
  • AR, MA, ARMA, ARIMA models
  • White noise
  • ACF and PACF
  • Stationarity
Hands-On:
  • Forecasting of TSA
  • Generation of ARIMA plot
  • Plot PACF and ACF
  • Implementation of the Dickey-Fuller test
  • Checking stationarity
  • Conversion of non-stationary data to stationary data
Learning Objective: In this module, you will learn about the selection of one model over the other models and the importance of boosting in machine learning.
Topics:
  • Model selection and its necessity
  • Cross-validation
  • Boosting and the working of its algorithm
  • Types of boosting algorithms and adaptive boosting
Hands-On:
  • AdaBoost
  • Cross-validation

Course Description

Our Python Certification Training for Data Science has been highly sought-out, with particular attention to specific topics to help you gain a Data Scientist's necessary skills.
Following topics will be covered throughout our Course: 

  • Overview of Python
  • Sequences and File Operations 
  • Deep Dive – Functions, Modules, OOPs, Errors and Exceptions 
  • Introduction to NumPy, Pandas and Matplotlib 
  • Data Manipulation 
  • Introduction to Machine Learning with Python
  • Supervised Learning - I 
  • Dimensionality Reduction 
  • Supervised Learning - II 
  • Unsupervised Learning 
  • Association Rules Mining and Recommendation Systems 
  • Reinforcement Learning 
  • Time Series Analysis 
  • Model Selection and Boosting
Python is the most famous language as it is widely used by data scientists for various scientific computations and machine learning applications. The compilation of python is pretty easy and its syntax is very simple for the users to understand. With the help of inbuilt Python debugger, it only takes a fraction of second to debug the programs. 

Who should do Python Data Science Certification Course?
  • This course is a perfect fit for:
  • Programmers
  • Developers
  • Architects
  • Technical Leads
  • Analytics Managers
  • Information Architects
  • Business Analysts
  • Python professionals

This course is a perfect fit for:

  • ·        Programmers
  • ·        Developers
  • ·        Architects
  • ·        Technical Leads
  • ·        Analytics Managers
  • ·        Information Architects
  • ·        Business Analysts
  • ·        Python professionals

Features

Frequently Asked Questions (FAQs):

There are no prerequisites for the Python Online Course Certification, and you only require a good Internet Connection with a Laptop to undertake this Course.
With this Python for Data Science Certification Training Course, you will gain an essential Certification in Data Science with Python, which will help you level up your familiarity with various tools and libraries in Python. 
Absolute beginners to Cloud can take up this course, either to improve their understanding of Python or to gain the Python Certification Training for Data Science.