• 48-49, 3rd floor, Jai Ambey Nagar, Opp. Jaipur Hospital, Tonk Rd, Jaipur
  • (+91) 8094336633
  • info@zeetronnetworks.com

Data Science Training in Jaipur

  • Data Science

    Data Science is the most preferred subject in the industry as of trend having a huge demand for professionals who have trained in it.

    There are so many data science algorithms to build predictive models, such as logistic regression, linear regression, decision trees, and random forests. The potential of data science is limitless – spanning across industries, having various roles and functions to perform.

    Data Science in its simpler terms is about generating critical business value from the data in various creative ways. It can also be defined as a mix of data research, algorithms, and technology in order to solve complex analytical issues.

  • Data Science Learning Outcomes

    • Develop hard skills in Data Science like Python, R Programming, Statistics, Machine Learning, Artificial Intelligence, Tableau, Deep Learning, Neural Networks, TensorFlow, Unix, Git, SQL.
    • Our Courses integrate real-world hands-on training with case studies & projects using Datasets from companies like Amazon, Facebook, Adobe, Walmart, etc.
    • Work on Real Projects, Build a Portfolio, Attend Interviews and Get Hired.
    • Certified Data Science Trainer with Years Real Time Industry Experience.
    • Best faculty with excellent Lab Infrastructure along with detailed course material.
    • Prepare your CV/Resume to attend Interviews and securing a Job.
    • We Share Common Interview FAQs, Interview Handling Skills & Real-Time Case studies.
    • One-to-one Attention by Instructors
    • Start learning data science through the promising Julia language and to become an efficient data scientist

What is Data Science?

Data science refers to the study of where information comes from, what it means and how it can be turned into valuable information in the creation of corporate businesses and IT strategies.

Mining large amounts of structured, unstructured and semi-structured data to identify relations can help an organization limit its costs, increase efficiencies, identify new market opportunities and enlarge the organization's competitive advantage.

Today, Data Science is a much-talked subject and moreover, its significance is being deliberated among the business managers who are eager to hire a brilliant professional onboard their firm. Data Science is a growing field and the demand for data scientists has been anticipated to increase this decade in the IT sector.

Statistics

  • Introduction to Data Science
  • The need for Data Science
  • BigData and Data Science
  • Data Science and machine learning
  • Data Science Life Cycle
  • Data Science Platform
  • Data Science Use Cases
  • Skill Required for Data Science
  • Linear Algebra
  • Vectors
  • Matrices
  • Optimization
  • Theory Of optimization
  • Gradients Descent
  • Descriptive vs. Inferential Statistics
  • Types of data
  • Measures of central tendency and dispersion
  • Hypothesis & inferences
  • Hypothesis Testing
  • Confidence Interval
  • Central Limit Theorem
  • Probability Theory
  • Conditional Probability
  • Data Distribution
  • Binomial Distribution
  • Normal Distribution

 

Python for Data Science

  • Why Python, its Unique Feature and where to use it?
  • Python Environment Setup/shell
  • Installing Anaconda
  • Understanding the Jupyter notebook
  • Python Identifiers, Keywords
  • Discussion about installed modules and packages
  • Python Data Types and Variable
  • Condition and Loops in Python
  • Decorators
  • Python Modules & Packages
  • Python Files and Directories manipulations
  • Use various files and directory functions for OS operations
  • Built-in modules (Library Functions)
  • Numeric and Math’s Module
  • String/List/Dictionaries/Tuple
  • Complex Data structures in Python
  • Python built-in function
  • Python user-defined functions
  • Array Operations
  • Arrays Functions
  • Array Mathematics
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy
  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs - Pivot
  • Combining DFs
  • Merge
  • Join
  • Data Alignment
  • Matrices Operations
  • Create matrices
  • Inverse, Transpose, Trace, Norms, Rank etc
  • Matrices Decomposition
  • Eigenvalues & vectors
  • Basics of Plotting
  • Plots Generation
  • Customization
  • Store Plots

 

Machine Learning

  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Trees (SingleTree)
  • Support Vector Machines
  • Model Ensemble
    • - Bagging
    • - Random Forest
    • - Boosting
    • -Gradient Boosted Trees
  • Model Evaluation and performance
    • - K-Fold Cross-Validation
    • - ROC, AUC, etc...
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  • Principal means Clustering
  • HierarComponents Analysis
  • Association Rules Mining & Market Basket Analysis
  • Basics
  • Term Document Matrix
  • TF-IDF
  • Twitter Sentiment Analysis