Data Science Course in Anantapur

Boost your Data Science career with the exclusive Data Science course in Anantapur  at 3zenx.

GCP Course in Hyderabad

  • 100% placement assistance
  • Hands on Assignments
  • Mock & Final Interview preparation
  • Practitioner’s Approach
  • Experienced faculty (20+ years)
  • Profile building/Resume Writing support


Data Science

“Dive into the universe fuelled by data with 3Zenx Data Science course training in Anantapur – where bytes of knowledge meet the art of analytics.
3Zenx Data science Course in Anantapur covers every aspect of the data science lifecycle, including data extraction, cleaning, exploration, transformation, feature engineering, data integration, data mining, building prediction models, data visualisation, and customer deployment. This Data Science training covers a wide range of skills and tools, including statistical analysis, text mining, regression modelling, hypothesis testing, predictive analytics, machine learning, deep learning, neural networks, natural language processing, predictive modelling, R Studio, Tableau, Spark, Hadoop, and programming languages like Python and R.
Data science is a tool, but it’s also an innovation trigger that’s brings a transformative change in every aspect of business. The importance of data science in today’s environment cannot be stated, from intelligent decision-making to predictive analytics and customised customer experiences it covers all.


  • Data Science Overview
  • Data Science
  • Data Scientists
  • Examples of Data Science in day to day life
  • Python for Data Science
  • Introduction to Data Visualization
  • Processes in Data Science
  • Data Wrangling, Data Exploration, and Model Selection
  • Exploratory Data Analysis or EDA
  • Data Visualization
  • Plotting
  • Hypothesis Building and Testing
  • Introduction to Statistics
  • Statistical and Non-Statistical Analysis
  • Some Common Terms Used in Statistics
  • Data Distribution
  • Methods of Central Tendency
  • Mean, Median, Mode
  • Methods of Dispersion
  • Percentiles, Dispersion
  • Histogram
  • Bell Curve
  • Hypothesis Testing
  • Chi-Square Test
  • Correlation Matrix
  • Inferential Statistics
  • Introduction to Anaconda
  • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
  • Jupyter Notebook Installation
  • Jupyter Notebook Introduction
  • Variable Assignment
  • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
  • Creating, accessing, and slicing tuples
  • Creating, accessing, and slicing lists
  • Creating, viewing, accessing, and modifying dicts
  • Creating and using operations on sets
  • Basic Operators: ‘in’, ‘+’, ‘*’
  • Logical operators
  • Functions
  • Use of break and continue keywords
  • Control Flow
  • Classes
  • Objects
  • Object oriented programming in python (encapsulation, abstraction, inheritance & polymorphism)
  • NumPy Overview
  • Properties, Purpose, and Types of ndarray
  • Class and Attributes of ndarray Object
  • Basic Operations: Concept and Examples
  • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
  • Copy and Views
  • Universal Functions (ufunc)
  • Shape Manipulation
  • Broadcasting
  • Linear Algebra
  • SciPy and its Characteristics
  • SciPy sub-packages
  • SciPy sub-packages –Integration Optimize
  • Linear Algebra
  • SciPy sub-packages – Statistics
  • SciPy sub-packages – Weave
  • SciPy sub-packages – I O
  • Introduction to Pandas
  • Data Structures
  • Series
  • DataFrame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read and Write Support
  • SQL Operation
  • Introduction to Machine Learning
  • Machine Learning Approach
  • How Supervised and Unsupervised Learning Models Work
  • Scikit-Learn
  • Supervised Learning Models
  • Linear Regression
  • Supervised Learning Models
  • Logistic Regression
  • K Nearest Neighbours (K-NN) Model
  • Unsupervised Learning Models – Clustering
  • Unsupervised Learning Models – Dimensionality Reduction
  • Pipeline
  • Model Persistence
  • Model Evaluation – Metric Functions
  • NLP Overview
  • NLP Approach for Text Data & Environment Setup
  • NLP Sentence analysis & Applications
  • Major NLP Libraries
  • Scikit-Learn Approach
  • Scikit – Learn Approach Built – in Modules
  • Scikit – Learn Approach Feature Extraction
  • Bag of Words
  • Extraction Considerations
  • Scikit – Learn Approach Model Training
  • Scikit – Learn Grid Search and Multiple Parameters
  • Pipeline
  • Introduction to Data Visualization
  • Python Libraries
  • Plots
  • Matplotlib Features:
  • –Line Properties Plot with (x, y)
  • –Controlling Line Patterns and Colors
  • –Set Axis, Labels, and Legend Properties
  • –Alpha and Annotation
  • –Multiple Plots
  • –Subplots
  • Types of Plots and Seaborn
  • Web Scraping
  • Common Data/Page Formats on The Web
  • The Parser
  • Importance of Objects
  • Understanding the Tree
  • Searching the Tree
  • Navigating options
  • Modifying the Tree
  • Parsing Only Part of the Document
  • Printing and Formatting
  • Encoding
  • Need for Integrating Python with Hadoop
  • Big Data Hadoop Architecture
  • MapReduce
  • Apache Spark
  • Resilient Distributed Systems (RDD)
  • PySpark
  • Spark Tools
  • PySpark Integration with Jupyter Notebook
Data Science by 3Zenx
Data Science Batches Starting soon

This Course Include

Skills Covered



Machine Learning

Cloud Computing


Frequently Asked Questions(FAQ'S)

For more information about Data Science please refer to the below questions

The proliferation of data science and the rise of big data and technology are both resulting in more career opportunities.

Given that data scientists work with huge data from leading tech organisations, it is tied to the IT industry.

Google produces large amounts of data every day, and data scientists are needed to manage this data effectively.

Let's be in the Lane of Digital Memories

“Make your memories last a lifetime with Data science skills from the top software training institutes in Hyderabad!”

A Word of Appreciation From Students

Read testimonials from students who have experienced our Quality

Batches We Offer For Software Courses in Hyderabad

Regular Batches

Regular Batches

If you are a student and can manage to come on a regular basis, then 3Zenx recommends that you enroll yourself in regular batches. Our schedule for regular batches is from Monday to Friday, five days a week.

Alternate Batches

Alternate Batches

If you would like to invest your time for practicing at home, then 3Zenx recommends you to enroll yourself for alternate batches. 3Zenx conducts alternate batches in which you need to come 3 Days a week on alternate basis.

Weekend Batches

Weekend Batches

If you are working or can’t manage to have free time on weekdays, then 3Zenx recommends you to enroll yourself for our weekend batches, which is only on Saturdays and Sundays.

Sunday Batches

Weekend Batches

In case if you are having a busy schedule from Monday to Saturday, then 3Zenx recommends you to enroll yourself for a Sunday Special Batch. However, you need to discuss the timings with our trainers.

Our Placement Process

Eligibility Criteria

Placements Training

Interview Q&A

Resume Preparation

Aptitude test

Mock Interviews

Scheduling Interviews

Job Placement