CertAchiever

Python Certification Training for Data Science

contact us: +918882136965

Instructor-led live online classes

Weekday Classes | Weekend Classes |

Python Certification Training for Data Science

$539

Python Certification Training for Data Science

Data Science using Python programming certification course enables you to learn data science concepts from scratch. 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. CertAchiever Python Certification Training course is also a gateway towards your Data Science career

Training Features

Instructor-led Sessions

Both Weekdays and Weekend batches are available.

Real-Life Case Studies

During the sessions you will be working on the case studies provided by the trainer.

Lifetime Access

You will get the life time access of training material including videos & study guides..

Assignments

Each class will be followed by practical assignments to practice the concepts covered in class

24 x 7 Expert Support

We have a lifetime 24x7 online support team to resolve all your technical queries,through a ticket based tracking system..

Certification

Towards the end of the course, you will be awarded with Training competition certification based on you performance.

Course Curriculum

Learning Objectives

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

Topics Covered

Overview of Python, 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

Creating “Hello World” code, Variables, Demonstrating Conditional Statements, Demonstrating Loops Fundamentals of Python programming, 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 Covered

Python files I/O Functions, 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

Tuple - properties, related operations, compared with a list, List - properties, related operations, Dictionary - properties, related operations, Set - properties, related operationsFile Operations using Python, 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 Covered

Functions, 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

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 PathError and Exception management in Python, 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 Covered

NumPy - arrays, 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

NumPy library- Creating NumPy array, operations performed on NumPy array, Pandas library- Creating series and data frames, Importing and exporting data, Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of PlotProbability Distributions in Python, Probability Distributions in Python, Python for Data Visualization

Learning Objectives

Through this Module, you will understand in detail about Data Manipulation

Topics Covered

Basic Functionalities of a data object, 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

Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging, Joining Python in Data Manipulation, Python in Data Manipulation

Learning Objectives

In this module, you will learn the concept of Machine Learning and its types.

Topics Covered

Python Revision (numpy, Pandas, scikit learn, matplotlib), 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

Linear Regression – Boston Dataset, Machine Learning concepts, 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 Covered

What are Classification and its use cases?, 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

Implementation of Logistic regression, Implementation of Logistic regression, Decision tree, Random forestSupervised Learning concepts, 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 Covered

Introduction to Dimensionality, Introduction to Dimensionality, Why Dimensionality Reduction, PCA, Factor Analysis, Scaling dimensional model, LDA

Hands On

PCA, PCA, Scaling Implementing Dimensionality Reduction Technique, 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 Covered

What is Naïve Bayes?, 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

Implementation of Naïve Bayes, SVM

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 Covered

What is Clustering & its Use Cases?, 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

Implementing K-means Clustering, Implementing Hierarchical Clustering Unsupervised Learning, 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 Covered

What are Association Rules?, 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

Apriori Algorithm, Market Basket Analysis Data Mining using python, 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 Covered

What is Reinforcement Learning, 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

Calculating Reward, Discounted Reward, Calculating Optimal quantities, Implementing Q Learning, Setting up an Optimal ActionImplement Reinforcement Learning using python, 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 Covered

What is Time Series Analysis?, 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

Checking Stationarity, Converting a non-stationary data to stationary, Implementing Dickey-Fuller Test, Plot ACF and PACF, Generating the ARIMA plot, TSA ForecastingTSA in Python, 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 Covered

What is Model Selection?, 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

Cross-Validation, AdaBoostModel Selection, Model Selection, Boosting algorithm using python

FAQ's

You will never miss a lecture at CertAchiever! You can choose either of the two options:

View the recorded session of the class available in your LMS.

·You can attend the missed session, in any other live batch."

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

You can give us a CALL at +918882136965 (IN) OR email at admin@certachiever.com

Certification

Once your training will complete, you will be awarded with training completion certification from the Trainer.