Software Technologies

Data Science Course

Turn your ambition into a high-paying career with our Data Science Course in Delhi. Learn Python, AI, and Machine Learning through real projects, earn a respected Data Science Certification, and step into the roles top companies are hiring for right now.

Shaym Sir KR Network Cloud
Instructor
Shyam Upadhyay

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9555378418, 8800272669

Email

info@krnetworkcloud.org
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    Data Science Course Overview

    Our Data Science Course is a complete, industry-focused learning program that gives you the technical expertise, analytical thinking skills, and real project experience needed to succeed in today’s data-driven world. Delivered by certified industry professionals with years of practical experience, this training takes you from essential foundations to advanced applications.

    You will start with Python programming for Data Science Course, Statistical analysis, and data visualization, then progress to data science integration, natural language processing, and machine learning model creation. Every lesson is supported with hands-on projects so you can apply your knowledge immediately. Whether you are a beginner entering the tech field or a working professional upgrading your skills, you will complete the program with a respected Data Science Certification and the confidence to solve real-world business challenges.

    Objective

    Course Objectives

    The objective of our data science course in delhi is to prepare learners to:

    • Understand the core principles, tools, and workflows of data science
    • Apply statistical methods, data analysis, and visualization to practical scenarios
    • Build and evaluate machine learning models for predictive insights
    • Integrate AI techniques into data-driven projects
    • Use Python with industry-standard libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn
    • Deliver solutions that help businesses make data-backed decisions.

    Skills You Will Gain

    • Python Programming for Data Science
    • Data preprocessing, cleaning, and transformation
    • Advanced statistical analysis and visualization
    • Machine learning model building and evaluation
    • AI and Data Science integration for real-world solutions
    • Web data extraction and automation

    Course Content

    Unit 1 - INTRODUCTION
    • Introduction to Data Science
    • Discussion
    • Data Science Process
    Unit 2 - STATISTICAL ANALYSIS
    • What Is Statistics?
    • Types of Statistics
    • Types of Data
    • Qualitative and Quantitative data
    • Measures of Central Tendency
    • SD 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
    • ANOVA
    • Calculus in Linear Algebra
    Unit 3 - LINEAR ALGEBRA & 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
    Unit 4 - WEB SCRAPING USING BEAUTIFULSOUP
    • Describe the basic terminology of web scraping
    • Basics understanding of HTML tags
    • Parser, objects, and function of Beautifulsoup
    Unit 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 filtering the Numpy array
    • Numpy Array Functions
    • Numpy Arithmetic Functions
    • Numpy Statistical Functions
    Unit 6 - DATA ANALYSIS 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 data frame
    Unit 7 - DATA VISUALIZATION USING MATPLOTLIB & SEABORN
    • Line plot
    • Set title, X-label, Y- label of plot
    • Subplotting
    • Bar plot
    • Scatter plot
    • Histogram
    • Boxplot
    • Pie chart
    • Heatmap
    • kdeplot
    Unit 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
    Unit 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 evaluation using accuracy score and confusion matrix
    • KNN
    • K-means clustering
    • Decision Tree
    • Overfitting and pruning
    • Random Forest Algorithms
    Unit 10 - NLP [NATURAL LANGUAGE PROCESSING (NLTK MODULE)]
    • Processing of text data
    • Tokenization
    • Lemmatization
    • Stemming vs lemmatization
    • Bag of words
    • NLP Project using naive Bayes algorithm
    PROJECT 1 - EDA OF IPL DATA AND COVID-19 DATA
    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 bicycles, stations, and the personnel required to maintain these. This demand is sensitive to many factors like season, humidity, rain, weekdays, holidays, and more. To enable this planning, Lyft needs to predict the demand according to these factors rightly
    PROJECT 3 - COMCAST TELECOM CONSUMER COMPLAINTS
    • Description
      • 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?

    1. As per Market Requirements

    Data science is among the most in-demand skills globally, with organizations across industries adopting data-driven strategies. From e-commerce and finance to healthcare and manufacturing, companies are actively hiring professionals who can analyze data, build predictive models, and optimize decision-making processes. Skilled data scientists often enjoy competitive salaries, rapid career growth, and opportunities to work on cutting-edge projects.

    1. As per Personal Skills and Career Goals

    Learning data science gives you strong analytical thinking, problem-solving ability, and technical expertise that remain valuable throughout your career. Whether you plan to switch into a tech role, enhance your current position, or launch a freelance or entrepreneurial venture, this skill set opens multiple opportunities and keeps your career future-proof.

    Data Science Career Benefits

    Graduates of our Data Science Course can step into roles such as Data Scientist, Machine Learning Engineer, AI Specialist, and Business Intelligence Analyst. These skills prepare you for opportunities in both domestic and international markets, with the potential to secure above-average salaries and long-term career stability.

    Want to get enrolled? Click Here to Submit your Details

    The Top Reason why to choose KR Network Cloud

    • KR Network Cloud is the Star Certified Authorized Training Partner
    • Live, interactive online sessions
    • Access to class recordings
    • Real-time working professionals and certified instructors
    • Practical learning with no-bluff teaching
    • Exclusive group access for live doubt support
    • Job assistance facility for our student is also available
    • We have a world-class experienced & Certified Trainer for data science training.
    • All lab facilities are available. labs are facilitated with computers.
    • We provide training as well as data science certification.
    • Demo session, Workshop, Exhibition, Back-Up Classes, Practice session, etc
    • Our trainer will also help to crack your interview

    Certificate

    Course Details
    • Shyam Upadhyay
      Instructor
    • Course Duration
      50 Hours
    • Batch Type
      Online Live Classes With Zoom Meeting
    • Email Address
      info@krnetworkcloud.org
    • Contact No.
      9555378418
    Submit Your Details
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