Python Certification Training for Data Science

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Data Science
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Master Data Science Using Python by enrolling in CertZip'S Python Certification Training for Data Science Certification Training and upskill your knowledge and technological skill in data science.

Course Description

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

Data science contains equipping data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis.

Programmers Developers Technical Leads Architects Developers desiring to be a Machine Learning Engineer Analytics Managers Business Analysts Learning (ML) Techniques Professionals who want to create automatic predictive models

Data scientists analyze which questions need answering and where to find the related data. They have business acumen and analytical skills and can mine, clean, and present data.

Basics of Computer Programming Languages. Fundamentals of Data Analysis Python Statistics for Data Science

Apache Spark is a robust analytics engine, and it is the most used Data Science tool. Spark is specifically created to handle batch processing and Stream Processing.

The primary objective of Data Science is to discover patterns within data. It uses diverse statistical methods to analyze and draw insights from the data. A data scientist must scrutinize the data thoroughly from data extraction, wrangling, and pre-processing.

Data Science is an adaptable field that has found applications in every enterprise, including healthcare, banking, e-commerce, business, and consultancy services.

What you'll learn

  • In this course, you will learn: different types of Machine Learning data operations file operations object-oriented programming Python libraries

Requirements

  • Basics of Python Programming Language. Fundamentals of Data Analysis Python Statistics for Data Science

Curriculam

Learn about the basics of data science.

Overview
The Companies using Python
Different Applications
Discuss Python Scripts on UNIX/Windows
Values, Types, Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Writing to the screen
Creating "Hello World" code
Variables
Demonstrating Conditional Statements
Demonstrating Loops

understand different types of sequence structures, related operations and their usage.

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
Tuple - properties, associated processes, compared with a list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

Discover to create generic scripts, how to address errors/exceptions in code, and finally how to extract/filter content using Regex.

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
Functions
Lambda
Sorting
Errors and Exceptions
Packages and Module

Learn the basics of statistics, different types of measures and probability distributions, and the supporting libraries that assist in these operations.

NumPy - arrays
Operations on arrays
Indexing slicing and iterating
Reading and writing arrays on files
Pandas - data structures & index operations
Reading and Documenting data from Excel/CSV formats into Pandas
matplotlib library
Grids, axes, plots
Markers, colours, fonts and styling
Types of plots
Contour plots
NumPy library
Pandas library
Matplotlib

Learn Data Manipulation.

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
Pandas Function- Ndim, axes, values, head, tail, sum, std, iteritems, iterrows, itertuples
GroupBy operations
Aggregation
Concatenation
Merging
Joining

know the concept of Machine Learning and its types.

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
Linear Regression – Boston Dataset

learn Supervised Learning Techniques and their implementation

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
Implementation of Logistic regression
Decision tree
Random forest

understand the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress sizes.

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

learn about Unsupervised Learning and the various types of Clustering used to analyze the data.

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

know Association rules and their extension towards recommendation engines with the Apriori algorithm.

What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How does Recommendation Engines work?
Collaborative Filtering
Content-Based Filtering
Apriori Algorithm
Market Basket Analysis

learn about developing an intelligent learning algorithm such that the learning becomes more and more accurate as time passes.

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
Calculating Reward
Discounted Reward
Calculating Optimal quantities
Implementing Q Learning
Setting up an Optimal Action

Understand Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyze accurate time-dependent data for forecasting.

What is Time Series Analysis?
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Stationarity
ACF & PACF
Checking Stationarity
Converting non-stationary data to stationary
Implementing Dickey-Fuller Test
Plot ACF and PACF
Generating the ARIMA plot
TSA Forecasting

Learn to select one model over another, Boost its importance in Machine Learning, convert weaker algorithms into stronger ones.

What is Model Selection?
The need for Model Selection
Cross-Validation
What is Boosting?
How do Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting
Cross-Validation
AdaBoost

FAQ

Data science does involve coding, and it does not require extensive knowledge of software engineering or advanced programming.

On average base salary is $95,000 per year.

To better understand data Science, one must learn as per the curriculum.

Data Scientist roles and responsibilities include identifying business trends using various techniques to interpret results from multiple data sources through statistical analysis, data aggregation, and data mining.

Yes, data science is a very good career with excellent options for improvement in the future. Already, demand is high, and salaries are competitive.

CertZip Support Unit is available 24/7 to help with your queries during and after completing Python Certification Training for Data Science Training.

You will receive CertZip Python Certification Training for Data Science Training certification on completing live online instructor-led classes. After completing Python Certification Training for Data Science course module, you will receive the certificate.

By enrolling in Python Certification Training for Data Science Training Certification course and completing the module, you can get CertZip Python Certification Training for Data Science Training Certification.

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Training Course Features

Assessments
Assessments

Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.

Forum
Forum

For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.

Certification
Certification

Successfully complete your final course project and CertZip will provide you with a completion certification.

Python Certification Training for Data Science

A Python Certification Training for Data Science certificate is a certification that verifies that the holder has the knowledge and skills required to work with Data science technology.

Yes, Access to the course material will be available for a lifetime once you have enrolled in the Edita Python Certification Training for Data Science Training Certification Course.

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