Software Technologies

Data Science Training

Get an array of professional opportunities and make important choices by mastering data science. Dive into the ever-changing world of data to explore personal development, innovation, and continuous learning.


Overview of Data Science

KR Network Cloud’s Data Science Training Program offers a thorough and interactive learning environment that covers foundational ideas, statistical analysis, machine learning, and real-world applications. This course is intended for those who want to become skilled data scientists with the ability to draw insightful conclusions from large, complicated datasets. Using industry-standard tools and methods, participants will acquire a solid foundation in statistical analysis, data manipulation,  Visualization, and machine learning. To guarantee that learned skills are applied practically, the program also places a strong emphasis on real-world projects


Data Science Course Objective

By the end of the course, participants will be proficient in leveraging various tools and techniques to extract valuable insights from data, make informed decisions, and build predictive models. The primary objective is to empower learners to apply data science methodologies to real-world scenarios, fostering a deep understanding of the entire data science lifecycle.

Course Content

Module 1 - Introduction
  • Introduction to Data Science
  • Discussion
  • Data Science Process
Module 2 - Statistical Analysis
  • What Is Statistics?
  • Types of Statistics
  • Types of Data
  • Qualitative and Quantitative data
  • Measures of Central Tendency
  • Standard Deviation and Variance for Population and Sample Data
  • How to Calculate the Variance and Standard Deviation?
  • Measures of Shape (Skewness)
  • Covariance and Correlation
  • Probability Distribution
  • Hypothesis Testing and mechanism
  • Hypothesis Testing Outcomes: Type I and II Errors
  • Null Hypothesis and Alternate Hypothesis
  • T-test and P-values in Python
  • Z-test and P-Values in Python
  • Chi-Square Distribution
  • Calculus in Linear Algebra
Module 3 - Linear Algebra and Probability Distribution
  • Introduction to Linear Algebra
  • Scalars and Vectors
  • Linear Independence of Vectors
  • Matrix
  • Transpose of a Matrix
  • The inverse of a Matrix, Eigenvalues, and Eigenvectors
  • Probability, Its Importance, and Probability Distribution
  • Probability Distribution: Bernoulli Distribution
  • Poisson Distribution
  • Normal Distribution
  • Central Limit Theorem
Module 4 - Web Scraping using BEAUTIFULSOUP
  • Describe basic terminology of web scraping
  • Basics understanding of HTML tags
  • Parser ,objects and function of Beautifulsoup
Module 5 - ND Arrays with Numpy
  • Explain Numpy uses
  • Numpy array vs list
  • How to create 1D,2D and ND array
  • Basic operations on Numpy array
  • Slicing and filter Numpy array
  • Numpy Array Functions
  • Numpy Arithmetic Functions
  • Numpy Statistical Functions
Module 6 - Data Analysing with Pandas
  • Uses of Pandas
  • How to create Series and DataFrame
  • Series Functions
  • DataFrame Functions
  • Import data from csv and excel
  • Descriptive and Statistical info about data
  • Check null value, unique value in data
  • Grouping and filtering data
  • Drop column, add column ,join dataframe
Module 7 - Data visualization using MATPLOTLIB and SEABOARD
  • Line plot
  • Set title , xlabel , ylabel of plot
  • Subploting
  • Bar plot
  • Scatter plot
  • Histogram
  • Boxplot
  • Paichart
  • Heatmap
  • Kdeplot
Module 8 - Feature engineering
  • Data Wrangling
  • Feature Selection
  • Data Pre-processing
  • Handling Missing value
    • CCA ( Complete Case Analysis)
    • Imputation
    • Mean Median imputation
    • Arbitrary value imputation
    • Mode imputation (category data)
    • Random imputation
  • Feature Scaling
    • Standardization
    • Normalization
  • Finding and Handling Outliers
  • Encoding
    • One Hot Encoding
    • Label Encoding
    • Ordinal Encoding
    • Binary Encoding
    • Frequency Encoding
Module 9 - Machine Learning with Scikit-learn
  • Introduction to Machine learning
  • Relationship between AI, ML, and data science
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Machine Learning
  • Model Building
  • Simple Linear Regression
  • Logistic Regression
  • Model evaluating using accuracy score and confusion matrix
  • KNN
  • K-means clustering
  • Decision Tree
  • Overfitting and pruning
  • Random Forest Algorithms
Module 10 - NLP (Natural language processing)
  • Processing of text data
  • Tokenization
  • Lemmatization
  • Stemming vs lemmatization
  • Bag of words
  • NLP Project using naive bayes algorithm
PROJECT 2 - Bike-Sharing Demand Analysis
  • Objective:
    • Use data to understand what factors affect the number of bike trips. Make a predictive model to predict the number of trips in a particular hour slot, depending on the environmental conditions.
  • Problem Statement:
    • Lyft, Inc. is a transportation network company based in San Francisco, California and operating in 640 cities in the United States and 9 cities in Canada. It develops, markets, and operates the Lyft mobile app, offering car rides, scooters, and a bicycle-sharing system. It is the second largest rideshare company in the world, second to only Uber.
    • Lyft’s bike-sharing service is also among the largest in the USA. Being able to anticipate demand is extremely important for planning of bicycles, stations, and the personnel required to maintain these. This demand is sensitive to a lot of factors like season, humidity, rain, weekdays, holidays, and more. To enable this planning, Lyft needs to rightly predict the demand according to these factors.
PROJECT 3 - Comcast Telecom Consumer Complaints

Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them $2.3 million, after receiving over 1000 consumer complaints. The existing database will serve as a repository of public customer complaints filed against Comcast. It will help to pin down what is wrong with Comcast’s customer service.

Why Learn Data Science?

  • High Demand for Data Professionals: In the digital age, data scientists provide the insights that are needed.
  • Data science expertise is broadly applicable, from finance to healthcare.
  • Conscientious Decision-Making
  • Career Possibilities: Many roles, such as scientist, engineer, and analyst, indicate strong demand.
  • Technological Progress: vital part in the development of AI, machine learning, and big data
  • An edge over competitors
  • Regular Education and Creativity
  • Tackles global issues, such as catastrophe forecasting and energy optimization
  • Valuable data handling skills are reflected in attractive salaries.
  • Transcends numbers and focuses on solving problems in the real world
  • Studying data science promotes development and sharpens critical thinking for a range of professionals

The Top Reason why to choose KR Network Cloud

  • KR Network Cloud is the Star Certified Authorized Training Partner
  • We have a world-class experienced & Certified Trainer for Data Science Training
  • All lab facilities are available. labs are facilitated with computer
  • We provide training as well as Data Science Certification
  • KR Network Cloud will provide you the Notes, Videos, and Data Science Training books
  • We provide corporate as well as industrial training in Delhi
  • Demo session, Workshop, Exhibition, Back-Up Classes, Practice session… ETC
  • Provide Exam Preparations to the Student
  • Our trainer will also help to crack your interview.
  • Job assistance facility for our student is also available
  • Provides online as well as classroom training.
  • Provides More Opportunity for future