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Data Science Training Course

Job Oriented Intensive Program

Advance your career with Quality Thought’s Data Science training! This course gives you Python, R, machine learning & data visualization skills. The Data Science training course also covers big data analytics. It works for beginners and those with experience already.

Flexible live classes are offered. (You can with or without video.) Learn at your own pace, whenever you want. You’ll master essential tools. Plus, you’ll get expert mentors and hands-on projects. Graduate fully ready to tackle real-world data challenges and lead innovation in your field!

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Data Science Training Course Upcoming Batches

Date:

4th, 14th 21st 28th November

Time

9:00 AM TO 06:00 PM

Program Duration:

110 Days

Learning Format:

Training

Course Curriculum

Date:

4th, 14th 21st 28th November

Time

11:00 AM TO 06:00 PM

Program Duration:

110 Days

Learning Format:

JOIP/I&I

Course Curriculum

India’s #1 Software Training Institute

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Master in Data Science Overview

Unlock Your Data Potential with Quality Thought’s Data Science Training Course!

We have a curriculum that covers everything. Yes, everything! From basic data analysis and programming to those fancy machine learning (oh, and big data) techniques everyone talks about. Get ready to dive into hands-on projects. They’re just like real-world problems.

But there’s more! You get personalized guidance from top industry experts. We mix comprehensive learning with practical experience. This means you will have all the skills you need to shine in the fast-paced world of Data Science.

Transform your career & become a data-driven leader with Quality Thought!

KEY HIGHLIGHTS

Comprehensive Data Science Fundamentals

Get a grip on the basics of data science, digging into numbers to make computers learn things. All with some experts.

Real-World Project Experience

Learn real skills by working on actual projects. You’ll face the same problems that folks do in real jobs.

Collaborative Learning Environment

Jump into a friendly and lively place where working together makes learning fun and easier!

Industry-Relevant Case Studies

Use real-life examples to use your data science know-how. Solve the problems that people face in industries today.

Data Science Overview

Comprehensive Training

Data Science Course Key Features

Skills Covered

Training Skills

Offered Programs

Exclusive Training

JOB ORIENTED INTENSIVE PROGRAM (JOIP)​

INTENSIVE & INTERNSHIP PROGRAM (I&I)

Pre Requisites

To shine in our Data Science Training Course, having some basic knowledge of statistics and math is super important. These are building blocks for understanding data. You’re also going to need to get cozy with programming, especially languages like Python or R. They’ll help you handle data & create models.

Knowing about databases and SQL? That’s a big plus! It’ll let you manage and query data so smoothly.

Also, having a strong problem-solving attitude and sharp analytical thinking will be your best pals. They help you tackle tough data problems. Plus, you’ll be able to use what you learn in real-world situations.

So, get ready! With these skills, you’re set to excel.

Course Curriculum

  • Introduction to Jupyter Notebook
  • Getting Started with Data Science
  • Unix Introduction
  • Python Basics
  • Python Introduction
  • Python Data Structure: Lists and Arrays
  • Python: Conditions and Branching
  • Python: Functions and Methods
  • Python: Objects and Classes
  • Practice Questions in Python
  • Introduction to NumPy
  • Linear Algebra in NumPy
  • Seaborn, Matplotlib
  • Project 1: Satellite Image Data Analysis using NumPy
  • Introduction to Pandas
  • Introduction to Probability
  • Probability Distributions
  • Describing Distributions
  • Probability Distribution with Multiple Variables
  • Population and Sample
  • Point Estimate
  • Confidence Interval
  • Hypothesis Testing
  • A/B Testing
  • Derivatives
  • Optimization
  • Gradients
  • Gradient Decent
  • Optimization in Neural Networks
  • Newton Methods
  • System of Linear Equations
  • Elimination Method
  • Row and Row Reduced Echelon form
  • Vector Algebra
  • Linear Transformation
  • Determinants
  • Eigen Values of Eigen Vectors
  • Array
  • String
  • Linked List
  • Searching Algorithm
  • Sorting Algorithm
  • Divide and Conquer Acqu
  • Stack
  • Queue
  • Tree Data Structures
  • Graph Data Structures
  • Dynamic Program
  • Data Acquisition
  • Data Wrangling
  • Data Statistical Analysis, Grouping and Correlation
  • Model Development
  • Model Evaluation and Refinement
  • Getting started in scikit-learn with the famous iris dataset
  • Training a Machine Learning Model with scikit-learn
  • Comparing Machine Learning Models in scikit-learn
  • Data Science Pipeline: Pandas, Seaborn, and scikit-learn
  • Cross-Validation for Parameter Tuning, Model Selection, and Feature Selection
  • Efficiently Searching for Optimal Tuning Parameters
  • Evaluating a Classification Model: Confusion Matrix and ROC
  • Basic Plotting for Data Visualisation
  • Data Manipulation for Visualisation
  • 1D Data Analysis: Histograms, Boxplots, and Violin Plots
  • Project 2: Visualization of world GDP and carbon dioxide emission
  • Project 3: Using Folium Library for Geographic Overlays
  • Introduction to Power-Bi
  • Data Extraction Process
  • Data Transformations
  • Data Modeling and DAX
  • Data Visualization with Analytics
  • Power-Bi, Q&A & Data Insights
  • Simple Linear Regression
  • Multiple Linear Regression
  • Non-Linear Regression
  • Regression Methods
  • Ridge Regression and Lasso Regression
  • Linear Regression and Decision Tree Regression
  • Random Forest Regression
  • Logistic Regression
  • Decision Tree Classification
  • Random Forest Classification
  • Boosting Algorithms
  • Bagging
  • K- Nearest Neighbours Classification
  • Naive Bayes Classification
  • K-Means Clustering
  • Hierarchical Clustering
  • K-Means and Hierarchical Clustering on the same dataset
  • Density-Based Spatial Clustering of Applications with Noise (DB-SCAN) Support Vector Machines & Regression
  • Principal Component Analysis (PCA)
  • Applying Principal Component Analysis on Handwritten Digits Dataset
  • Market Basket Analysis
  • Evaluate the speed, runtime and memory dependencies of algorithmic models Parallel computing systems such as SISD (Single Instruction Single Data Stream), SIMD (Single Instruction Multiple Data Streams), MISD (MultipleInstructions Single Data Stream), MIMD (Multiple Instructions Multiple DataStreams)
  • How to use coding tools
  • Create, review and execute unit test cases Corrective and Preventive actions for problems and defects can improve future designs
  • Measure and Optimize performance of algorithm
  • Deployment of the Models
  • Why Choose Quality Thought

    100% Success Rates in the Placement for Skilled People

    A gate way to your🤔 Bright Future in the IT industry

    Connect with us for Life-changing opportunities

    Testimonials

    The Data Science course from Quality Thought changed my job! The instructors—total pros—with a curriculum that covered all Practical tasks. They made learning so easy.

    Anjali Reddy.

    I had an amazing experience! Quality Thought’s hands-on approach, plus their individual attention, helped me succeed in Data Science.

    Samrat Iyer

    The course went way beyond what I expected. It had fun discussions, well-organized modules & awesome placement help. I highly recommend Quality Thought.

    Rahul Dixit

    The Data Science course by Quality Thought is great. The practical assignments, along with dedicated instructors, were key to my growth.

    Vrinda Rao

    Key Facts Of Quality Thought

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    Job Oriented Intensive Program (JOIP)

    student oriented

    50000+ Students Trained

    students levels

    15000+ Students Placed at Different Levels

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    Training by Realtime Industry Experts

    Company

    Tie Up With 250+ Compaines

    bank

    Educated 15+ BPO & Back Office/Ops on IT Trends

    Certification

    Quality Thought has a quick & easy Data Science certification process for students who finish their training. To earn the certification, they must turn in all projects and assignments on time. The certification, showing how hard they’ve worked and what they’ve learned, will be given a week of course completion. This helps students add this important credential to their resumes right away. It shows their dedication and skill in Data Science. Quality Thought wants to honor its students’ efforts & help them move forward with a valued certification.

    qualitythought-certificate

    Frequently asked questions

    To sign up for a Data Science course, you usually need a bachelor’s degree in a related. This could be computer science, mathematics, statistics, or engineering It’s super important to have a solid background in math, especially in probability & statistics. You should also know your way around databases and be good with computer languages like R or Python. Some schools might want you to have some with machine learning or data analysis tech too.

    Plus, being good at critical thinking and solving problems is good (it’s pretty much essential!). And having a strong desire to make choices based on evidence. That’s a big plus!

    Yes, Data Science is a fantastic field for freshers! There are so many opportunities, & it’s in demand across lots of different industries. Freshers with a good handle on math, programming, & analytical skills can do well here. You see (and this is important), many entry-level roles, like Data Analyst or Junior Data Scientist, open doors to grow in this field.

    The mix of different subjects in Data Science helps freshers learn and USE skills in real-world situations. That makes it exciting AND rewarding. Plus, there’s amazing growth potential!

    Yes, Data Science is seen as safe for the future. The demand for data-driven choices is rising in many fields, so Data Science is a critical & growing area. With the constant rise of big data, artificial intelligence, and machine learning, skilled data scientists are needed.

    Data Science gives you strong job security, lots of career options, and the chance to earn well. As businesses use more data to compete, Data Science will stay an important and future-proof career path. So, if you’re thinking about it, it’s a solid choice for your future.

    Freshers in data science can dive into all sorts of careers. One might become a Data Analyst. They could also for a Junior Data Scientist spot. Maybe even work as a Business Intelligence Analyst.

    These jobs usually mean cleaning data, doing analysis & making visualizations. It’s a great start for bigger roles later on.

    But wait, there’s more! Freshers can also work as Data Engineers. This means helping with data infrastructure and Making pipelines (super cool stuff).

    Internships? Entry-level positions? Yep, they give hands-on experience, too! Tons of learning & growing. It’s the path to big things like machine learning, AI, or even more data engineering fun. This is the beginning of an awesome career journey.

    Freshers stepping into Data Science in India can expect a starting pay between ₹4,00,000 and ₹8,00,000 per year. Of course, this number can change. It depends on where you are, which company you join & what skills and education you have.

    In big tech cities like Bengaluru or Hyderabad? Well, your starting salary could be on the higher side. Cool, right?

    As you gather more experience and grow (get better) in your job, your earnings will likely go up too. Yes, there are plenty of chances for bigger paychecks as you climb the ladder in the data science world.

    Ready to get Data Science JOB?

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