Certification in Data Science with Python Training

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Certification in Data Science with Python Training

DevLabs Alliance brings you a comprehensive Data Science certification course in Python that enables you to understand Python programming language from basics through advanced topics. This course will help you comprehend important Python programming concepts such as Data & File Operations, Object-Oriented Concepts in Python & various Python libraries such as Matplotlib, Pandas, and Numpy, etc., hence, enabling you to delve into Data Science. As a result of it, you gain proficiency in various EDA and Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes, and Q-Learning.

+40 Hours

Practical Session

1000

Students Enrolled

Advanced

Level

12

Modules

About Certification in Data Science with Python Training

DevLabs Alliance’s Data Science Certification – Python Course will primarily cover the concepts of Python like object-oriented concepts, sequences, file operations and some of the extensively used Python libraries which include pandas, numpy, matplotlib, etc. and steadily converge towards Machine Learning and its detailed mechanism.

As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be trained on Reinforcement Learning, which in turn is a vital characteristic of Artificial Intelligence. You will be able to learn your machine based on real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.

Who Can Take This Course

Programmers, Software Developers, Software Testing Professionals, Technical Leads, Architects

Suitable For

IT Leaders who want to set the direction of their organization in Data Science and Machine Learning
Developers aspiring to be a 'Machine Learning Expert'
Graduates who are looking to make a career in Data Science
Any professional who wants to understand how a predictive analysis system works

Total Skills Covered

Learn data visualization techniques

Learn techniques to handle various types of data – ordinal, categorical, encoding

Learn different tools and methods for predictive modeling

Understand Roles played by a Machine Learning Engineer

Perform Sentimental analysis

Programmatically download data and analysis on it

Work with real-time numbers

Understand Time Series and its associated concepts

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Case Study Discussions

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Get answers to all your queries with an interactive Q&A session with our experts. Clarify your doubts and get a deeper insight.

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Pay your course fee through flexible EMI plans, with no financial constraints in your learning journey.

1 to 1 Training Sessions

In 1 to 1 training sessions our experts ensure dedicated attention and a tailored learning style to fit your needs.

Certification in Data Science with Python Certification

To unlock DevLabs Alliance’s certification, you have to complete and submit the use case assignments provided during the course, then assignments submitted by you will be evaluated by DevLabs Alliance’s team of experts and practitioners. Your result will be shared after assessment and certification will be awarded accordingly.

Sample Certificate

DevLabs Alliance's certified professionals do work for prominent international corporations such as Accenture, Optum, Capgemini, Cognizant, TCS, Wipro, IBM, Sapient, Nucleus, Oracle, Expedia, and more.
To gain access to DevLabs Alliance's courses and their corresponding certifications, you need to adhere to the following steps:

Successfully complete and submit the practical assignments assigned during the course.
The DevLabs Alliance team of seasoned experts and practitioners will assess the assignments you submit.
Your assessment results will be communicated to you, and certification will be granted accordingly.

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Program Curriculum

Total Chapters 12

1: Introduction to Python, its Sequences and File Operations

Objectives:

At the end of this Module, you should be able to:

Define Python

Understand why Python is Popular

Setup Python Environment

Understand Operands and Expressions

Write your First Python Program

Understand Command Line Parameters and Flow

Control

Take Input from the User and Perform Operations on it

Explain Numbers

Explain Strings, Tuples, Lists, Dictionaries, and Sets

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

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

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

Objectives:

Create and Execute Functions

Learn Object Oriented Concepts

Understand Python Standard Libraries

Define Modules

Handle the Exceptions

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

3: Data Manipulation using NumPy, Pandas & Matplotlib

Objectives:

Create Arrays using NumPy

Perform Various Operations on Arrays and Manipulate them

Read & Write Data from Text/CSV Files into Arrays and vice versa

Create Series and Data Frames in Pandas

Indexing and Slicing of Data Structures in Pandas

Reading and Writing Data from Excel/CSV Formats into Pandas

Data Preparation

Merging, Concatenation, Combining, Pivoting & Removal

Data Transformation – Merging, Joining &

Concatenation

Create Simple Plots using Matplotlib

Learn Different Plot Formats Available in Matplotlib

Choose the Right Plot Format for a Problem at Hand Judiciously

Scale and Add Style to your Plots

Topics:

NumPy – Arrays

Operations on Arrays

Indexing, Slicing and Iterating

Reading and Writing Arrays on Files

Pandas – Data Structures & Index Operations

Basic Functionalities of a Data Object

Merging of Data Objects

Concatenation of Data Objects

Types of Joins on Data Objects

Exploring a Dataset

Analyzing a dataset

Reading and Writing Data from Excel/CSV Formats with

Pandas

The Matplotlib Library

Grids, Axes, Plots

Markers, Colors, Fonts and Styling

Types of Plots – Bar Graphs, Pie Charts, Histograms

Contour Plots

4: Introduction to Machine Learning with Python

Objective:

Essential Python Review

Necessary Machine Learning Python libraries

Define Machine Learning

Discuss Machine Learning Use cases

List the categories of Machine Learning

Illustrate Supervised Learning Algorithms

Identify and recognize machine learning algorithms around us

Understand the various elements of machine learning algorithms like parameters, hyperparameters, loss function and optimization.

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

5: Supervised Learning – I

Objective:

Understand What is Supervised Learning?

Illustrate Logistic Regression

Define Classification

Explain different Types of Classifiers, such as,

Decision Tree

Random Forest

Topics:

What is 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?

6: Dimensionality Reduction

Objective:

Define the importance of Dimensions

Explore PCA and its implementation

Discuss LDA and its implementation

Topics:

Introduction to Dimensionality

Why Dimensionality Reduction

PCA

Factor Analysis

Scaling dimensional model

LDA

7: Supervised Learning – II

Objective:

Understand What is Naïve Bayes Classifier

How Naïve Bayes Classifier works?

Understand Support Vector Machine

Illustrate How Support Vector Machine works?

Understand Hyper parameter Optimization

Topics:

What is Naïve Bayes?

How Naïve Bayes works?

Implementing Naïve Bayes Classifier

What is a Support Vector Machine?

Illustrate how Support Vector Machine works?

Hyperparameter Optimization

Grid Search vs Random Search

Implementation of Support Vector Machine for

Classification

8: Unsupervised Learning

Objective:

Define Unsupervised Learning

Discuss the following Cluster Analysis

K – means Clustering

C – means Clustering

Hierarchical Clustering

Topics:

What is Clustering & its Use Cases?

What is K-means Clustering?

How K-means algorithm works?

How to do optimal clustering

What is C-means Clustering?

What is Hierarchical Clustering?

How Hierarchical Clustering works?

9: Association Rules Mining and Recommendation Systems

Objective:

Define Association Rules

Learn the backend of recommendation engines and develop your own using python

Topics:

What are Association Rules?

Association Rule Parameters

Calculating Association Rule Parameters

Recommendation Engines

How Recommendation Engines work?

Collaborative Filtering

Content Based Filtering

10: Reinforcement Learning

Objective:

Explain the concept of Reinforcement Learning

Generalize a problem using Reinforcement Learning

Explain Markov’s Decision Process

Demonstrate Q Learning

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

values

11: Time Series Analysis

Objective:

Explain Time Series Analysis (TSA)

Discuss the need of TSA

Describe ARIMA modelling

Forecast the time series model

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

12: Model Selection and Boosting

Objective:

Discuss Model Selection

Define Boosting

Express the need of Boosting

Explain the working of Boosting algorithm

Topics:

What is Model Selection?

Need of Model Selection

Cross – Validation

What is Boosting?

How Boosting Algorithms work?

Types of Boosting Algorithms

Adaptive Boosting

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