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Python Certification Training for DataScience

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 StepLeaf’s Python Certification Training for DataScience Course is a kickstart to learn zero knowledge python programming and write down Python Scripts. This course gives in-depth understanding of data structures, Python programming fundamentals and working with data in Python

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Python Certification Training for Data Science

Jul 25 Sat,Sun (10.5 Weeks) Weekend Batch 01:30 AM  03:30 AM
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Course Price at

$ 669.00

About Course

 StepLeaf’s Python Certification Training for DataScience Course helps you to perform hands-on analysis using Python from Scratch. The data analysis is done in a Jupyter-based lab environment. It helps you to create your own data science projects.

Course Objectives

This course is a benchmark for you to build a complex model in ease. It helps you to learn uncanny things and a good threshold to work with.

  • Understand the fundamental of data science
  • Understand the fundamental of data analytics
  • Work with statistical analysis and business applications
  • Write scripts in python
  • Work with mathematical computing with Python (NumPy)
  • Work with Scientific computing with Python (SciPy)
  • Ability to work on data manipulation with pandas
  • Understand Machine learning with Scikit
  • Understand Natural Learning Processing with Scikit
  • Work with data visualization in Python using matplotlib
  • Understand web scraping with BeautifulSoup
  •  Able to integrate Python with Hadoop MapReduce and Spark

Who should take up this Certification Course?

StepLeaf’s Python for DataScience Course is mainly preferred for Analytics Manager, Software Developers, Business Analytics, Integration specialists, Information Architects and Python professionals.

What are the prerequisites for this course?

Little knowledge in the basics of Computer programming will explode your learning into a masterpiece.


Key Skills

Python, numpy, matplotlib, pandas, exceptionmanagement, functions, lambda, machinelearning, linearregression, gradientdescent, randomforest, confusionmatrix, decisiontree, dimensionalityreduction, naïvebayes, svm, supportvectormachine, associationrules, reinforcement, tsa, modelselection

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Course Contents

Download Syllabus

Python Certification Training for DataScience Content

Learning Objectives: You will get a brief idea of what Python is and touch on the basics.  

Topics:

  • Overview of Python
  • The Companies using Python
  • Different Applications where Python is used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the screen

Hands On/Demo:

  • Creating “Hello World” code
  • Variables
  • Demonstrating Conditional Statements
  • Demonstrating Loops

Skills:

  • Fundamentals of Python programming


Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.
Topics:
  • Python files I/O Functions
  • Numbers
  • Strings and related operations
  • Tuples and related operations
  • Lists and related operations
  • Dictionaries and related operations
  • Sets and related operations
Hands On/Demo:
  • Tuple - properties, related operations, compared with a list
  • List - properties, related operations
  • Dictionary - properties, related operations
  • Set - properties, related operations

Skills:

  • File Operations using Python
  • Working with data types of Python
Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
Topics:
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda Functions
  • Object-Oriented Concepts
  • Standard Libraries
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions
Hands On/Demo:
  • Functions - Syntax, Arguments, Keyword Arguments, Return Values
  • Lambda - Features, Syntax, Options, Compared with the Functions
  • Sorting - Sequences, Dictionaries, Limitations of Sorting
  • Errors and Exceptions - Types of Issues, Remediation
  • Packages and Module - Modules, Import Options, sys Path
Skills:
  • Error and Exception management in Python
  • Working with functions in Python
Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
Topics:
  • NumPy - 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, colours, fonts and styling
  • Types of plots - bar graphs, pie charts, histograms
  • Contour plots
Hands On/Demo:
  • NumPy library- Creating NumPy array, operations performed on NumPy array
  • Pandas library- Creating series and dataframes, Importing and exporting data
  • Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot
Skills:
Probability Distributions in Python
Python for Data Visualization

Learning Objective: Through this Module, you will understand in detail about Data Manipulation
Topics:
  • Basic Functionalities of a data object
  • Merging of Data objects
  • Concatenation of data objects
  • Types of Joins on data objects
  • Exploring a Dataset
  • Analysing a dataset
Hands On/Demo:
  • Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
  • GroupBy operations
  • Aggregation
  • Concatenation
  • Merging
  • Joining
Skills:
  • Python in Data Manipulation

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.
Topics:
  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent
Hands On/Demo:
  • Linear Regression – Boston Dataset
Skills:
  • Machine Learning concepts
  • Machine Learning types
  • Linear Regression Implementation

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
  • What are Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
Hands On/Demo:
  • Implementation of Logistic regression
  • Decision tree
  • Random forest
Skills:
  • Supervised Learning concepts
  • Implementing different types of Supervised Learning algorithms
  • Evaluating model output
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Topics:
  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA
Hands-On/Demo:
  • PCA
  • Scaling
  • Skills:
  • Implementing Dimensionality Reduction Technique

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification
Hands-On/Demo:
  • Implementation of Naïve Bayes, SVM
  • Skills:
  • Supervised Learning concepts
  • Implementing different types of Supervised Learning algorithms
  • Evaluating model output

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Topics:
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does K-means algorithm work?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?
Hands-On/Demo:
  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering
Skills:
  • Unsupervised Learning
  • Implementation of Clustering – various types

Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
Topics:
  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How does Recommendation Engines work?
  • Collaborative Filtering
  • Content-Based Filtering
  • Hands-On/Demo:
  • Apriori Algorithm
  • Market Basket Analysis
Skills:
  • Data Mining using python
  • Recommender Systems using python

Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.
Topics:
  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • α values
Hands-On/Demo:
  • Calculating Reward
  • Discounted Reward
  • Calculating Optimal quantities
  • Implementing Q Learning
  • Setting up an Optimal Action
Skills:
  • Implement Reinforcement Learning using python
  • Developing Q Learning model in python

Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
Topics:
  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF
Hands on/Demo:
  • Checking Stationarity
  • Converting a non-stationary data to stationary
  • Implementing Dickey-Fuller Test
  • Plot ACF and PACF
  • Generating the ARIMA plot
  • TSA Forecasting
Skills:
  • TSA in Python

Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
Topics:
  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting
Hands on/Demo:
  • Cross-Validation
  • AdaBoost
Skills:
  • Model Selection
  • Boosting algorithm using python

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Projects

What are the system requirements for our Python Certification Training for DataScience?

The practical training is done in a Cloud Lab environment. This environment already has the required software in it.

How will I execute my practicals? 

The Case Studies are executed using Jupyter Notebook in Cloud Lab. The necessary instruction will be given by our StepLeaf instructor to execute all the assignments. 

What are the Case studies for this course? 

There are totally 40 case studies as a part of this training. Given below are few of them. 

Case Study 1 

An Online Hotel booking application wants to create a recommendation of optimal suggestions for the users to book a hotel. Predict the hotel cluster for the user to book a room in a hotel using multi-class classification problems, build SVM and decision tree. 

Case Study 2 

In an Online Public Library users are requested to search for their individual choice of book. Using the ML model suggests that users read some more books based on his past purchase and refer to similar books read by other users. Help the library to find the error in their approach and build a profitable application. 

Case Study 3 

Do an end-to-end case study using time series analysis and forecasting with ML using Python. Extract meaningful statistics and find the insight of the data to predict future value with observed values. 

Case Study 4 

A construction company had a problem with its clients based on the quality of the building being constructed. Do an analysis and figure out all the different department in constructing a building and discover the problem and efficiency in each department which hinders the quality. Implement a proper solution to the problem 

What are the projects for this course? 

There are totally 5 projects as a part of this training. Given below is one of them. 

Description 

1. Download and install Python SciPy  

2. Load dataset

3. Create 6 Machine Learning models, find the best with accuracy



StepLeaf’s Python for Data Science Professional Certificate Holders work at 1000s Of MNC Companies All Over the World

FAQ

Online learning is a mixture of live tutoring and recorded videos. It helps you to complete on your own time and give much flexibility to the students. Finally, you can say that it just fits your needs.  


StepLeaf uses a blended learning technique which consists of auditory, visual, hands-on and much more technique at the same time. We assess both students and instructors to make sure that no one falls short of the course goal.

Yes, we offer crash courses. You could get the overview of the whole course and can drive it within a short period of time.

Currently we don't offer demo class as the number of students who attend the live sessions are limited. You could see our recorded video of the class in each course description page to get the insight of the class and the quality of our instructors.

StepLeaf has a study repository where you can find the recorded video of each class and all other essential resources for the course. 

Each student who joins StepLeaf will be allocated with a learning manager to whom you can contact anytime to clarify your queries

Yes we have a centralized study repository, where students can jump in and explore all the latest materials of latest technologies.

Assessment is a continuous process in StepLeaf where a student's goal is clearly defined and identifies the learning outcome. We conduct weekly mock tests, so that students can find their shortfalls and improve them before the final certification exam.

StepLeaf offers a discussion board where students can react to content, share challenges, teach each other and experiment their new skills.

You can pay your course fee online quickly through secure Razorpay gateway. You will be able to track the payment details on the way.

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