Software Technologies

Data Science Training

This Data Science training equips you to analyze real-world data. Master statistics,
web scraping, data manipulation with NumPy & Pandas, and create visualizations with
Matplotlib & Seaborn

Instructor
Contact Us

Call Us

9555378418, 8800272669

Email

info@krnetworkcloud.org

Overview of Data Science

 If you’re looking to become a part of this exciting field, KR Network Cloud’s Data Science Training Program offers a thorough and interactive learning environment to get you started.

Data Science Course Objective

KR Network Cloud’s course covers foundational concepts, statistical analysis, machine learning, and real-world applications. This program is perfect for those aiming to become proficient data scientists capable of deriving insightful conclusions from complex datasets. Using industry-standard tools and methods, participants will gain a solid grounding in statistical analysis, data manipulation, visualization, and machine learning. The program emphasizes real-world projects to ensure practical application of learned skills

Objective

Data Science Course Objective

This Data Science training equips you to analyze real-world data with a comprehensive skill set. You will:

  • Master statistical analysis to interpret and manage data effectively.
  • Gain proficiency in web scraping using BeautifulSoup to gather data from various sources.
  • Develop skills in data manipulation with NumPy and Pandas, essential for any data scientist.
  • Create compelling data visualizations with Matplotlib and Seaborn to present data insights.
  • Learn feature engineering techniques to enhance your data models.
  • Build machine learning models using Scikit-learn, covering regression, classification, and clustering.
  • Get exposure to Natural Language Processing (NLP) with NLTK, enabling you to work with textual data.
  • Solidify your skills through hands-on projects, preparing you for real-world data analysis tasks.

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 SCRAPPING 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 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 data frame
Unit 7 - DATA VISUALIZATION USING MATPLOTLIB & SEABORD
  • Line plot
  • Set title, X-label, Y- label of plot
  • Subploting
  • Bar plot
  • Scatter plot
  • Histogram
  • Boxplot
  • Paichart
  • 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?

  • High Demand for Data Professionals
  • Broad Applicability
    • Data science skills are needed across various industries, from finance to healthcare.
  • Conscientious Decision-Making
    • Data science enables informed and thoughtful decisions.
  • Career Possibilities
    • Opportunities abound in roles such as data scientist, engineer, and analyst.
  • Technological Progress
    • A vital component in the advancement of AI, machine learning, and big data.
  • Competitive Edge
    • Stand out in the job market with in-demand skills.
  • Continuous Learning and Innovation
    • Stay ahead in a field that’s always evolving.
  • Global Impact
    • Address global challenges like disaster prediction and energy optimization.
  • Attractive Salaries
    • Reflecting the value of data handling skills.
  • Problem-Solving
    • Beyond numbers, data science focuses on real-world problem-solving.
  • Critical Thinking
    • Enhance your ability to analyze and approach various problems critically.

Attend a free demo – Click Here!

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
Course Details
  • Instructor
  • Course Duration
  • Batch Type
    Online Live Classes With Zoom Meeting
  • Email Address
    info@krnetworkcloud.org
  • Contact No.
    919555378418
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