Data Science Online Training Jntu Hyderabad

Data Science and Machine Learning  Demo By Balaji:https://goo.gl/ZHrcdG

Data Science training Hyderabad and Machine Learning Course Content and Course fees :Please Go Down

Data Science Training Hyderabad Vlr Training

VLR Training provides Data Science training. we are in KPHB, Hyderabad.
Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. For More Info https://en.wikipedia.org/wiki/Data_science

About Data Science Training Hyderabad online Course Details

Faculty Name: Balaji (His native place is  Andhra Pradesh(India). @ present in Chicago (USA))

Course Duration: 120Hours (4Months)

Course Fees: 27000 Rs(Negotiable)

Contact:+91 9059868766

Note* Everyday session recordings are also available

Watch Data Science training demo Videos https://www.youtube.com/embed/uaqzbLPU5K8?list=PLXx2-0oYp1LMflBs6euVJnoWIKoRunvFy

Who Can get data science training

Everyone can get data science training.

  • Fresher’s/Graduates
  • Job Seekers
  • Managers
  • Data analysts
  • Business analysts
  • Operators
  • End users
  • Developers
  • IT professionals

Data Science Training Hyderabad Course Content

01)Introduction to Data Science and Python

  • 1. Python Basics with Anaconda
  • 2. Files and Loops
  • 3. Booleans and If Statements
  • 4. Files loops and Condition Logics with Application Example
  • 5. List Operations, Dictionaries
  • 6. Introduction to Functions
  • 7. Debugging Errors
  • 8. Project: Exploring US Date Births
  • 9. Modules, Classes
  • 10. Error Handling
  • 11. List Comprehensions
  • 12. Project: Modules, Classes, Error Handling, List Comprehensions by Using NFL Suspension Data
  • 13. Variable Scopes
  • 14. Regular Expressions
  • 15. Dates in Python
  • 16. Project: Exploring Gun Deaths in US

02 Data Analysis and Visualization

  • 1. Getting Started with Numpy
  • 2. Computation with Numpy
  • 3. Introduction to Pandas
  • 4. Data Manipulation with Pandas
  • 5. Working with Missing Data
  • 6. Project: Summarizing Data
  • 7. Pandas Internal Series
  • 8. Data Frames in Pandas
  • 9. Project: Analyzing Thanks Giving Dinner
  • 10. Project: Finding Patterns in Crime
  • Exploratory Data Visualization
  • 11. Line Charts
  • 12. Multiple Plots
  • 13. Bar Plots and Scatter Plots
  • 14. Histograms and Box Plots
  • 15. Project: Visualizing Earnings based on college Majors
    Story Telling Through Visualization
  • 16. Improving Plot Aesthitics
  • 17. Color Layout and Annotations
  • 18. Project: Visualizing Gender Gaps in Colleges
  • 19. Conditional Plots
  • 20. Project:Visualizing Geographical Data

03 Data Cleaning

  • 1. Data Cleaning Walkthrough
  • 2. Data Cleaning Walkthrough Combining the data
  • 3. Analyzing and Visualizing the Data
  • 4. Project: Analyzing NYC High School Data
  • 5. Project: Star Wars Survey

04 Working with Data Sources

————— 1. APIS and Web Scrapping
  • (I) Working with APIS
  • (II) Intermediate APIS
  • (III) Working with REDDIT API
  • (IV) Web Scrapping
————— 2. SQL Fundamentals
  • (I) Introduction to SQL
  • (II) Summary Statistics
  • (III) Group Summary Statistics
  • (IV) Querying SQLITE from Python
  • (V) Project: Analyzing CIA Facebook Data Using SQLITE and Python
————— 3. SQL Intermediate
  • (I) Modifying Data
  • (II) Table Schemas
  • (III) Database Normalization and Relations
  • (IV) Postgre SQL and Installation
————— 4. Advanced SQL
  • (i) Indexing and Multicolumn Indexing
  • (ii) Project: Analyzing Basketball data

05 Statistics and Probability

  • 1. Introduction to Statistics
  • 2. Standard Deviation and Correlation
  • 3. Linear Regression
  • 4. Distributions and Sampling
  • 5. Project: Analyzing Movie Reviews
  • 6. Introduction to Probability
  • 7. Calculating Probabilities
  • 8. Probability Distributions
  • 9. Significance Testing
  • 10. Chi Squared Test
  • 11. Multi Category Chi Squared Test
  • 12. Project: Wining Jeopardy

06 Machine Learning

  • 1. Machine Learning Fundamentals
  • 2. Introduction to KNN
  • 3. Evaluating Model Performances
  • 4. Multivariate KNN
  • 5. Hyper Parameter Optimization
  • 6. Cross Validation
  • 7. Project: Predicting Car Prices
  • 8. Calculus for Machine Learning
  • 9. Understanding Extreme points, limits and Linear & Nonlinear Functions
  • 10. Linear Algebra (Linear Systems, Matrices, vectors, Solution Sets)
  • 11. Linear Regression Model
  • 12. Feature Selection
  • 13. Gradient Descent
  • 14. Ordinary Least Squares
  • 15. Processing and Transforming Features
  • 16. Project:Predicting House sales Prices
  • 17. Logistic Regression
  • 18. Evaluating Binary Classifiers
  • 19. Multiclass Classification
  • 20. Intermediate Linear Regression
  • 21. Overfitting
  • 22. Clustering Basics
  • 23. K-Means Clustering
  • 24. Gradient Descent
  • 25. Introduction to Neural Networks
  • 26. Project: Predicting the Stock Market
  • 27. Introduction to Decision Trees
  • 28. Building, Applying Decision Trees
  • 29. Introduction to Random Forest
  • 30. Project: Predicting Bike Rentals

Machine Learning Projects
1. Data Cleaning
2. Preparing Features
3. Making Predictions
4. Sentiment Analysis

07 Spark and Map Reduce

  • 1. Introduction to Spark
  • 2. Spark integration with Jupyter
  • 3. Transformations and Actions
  • 4. Spark Data Frames
  • 5. Spark SQL

08 Building a Capstone Project

Data Science training Hyderabad Batch 02 Videos

Data Science training Hyderabad Batch 01 Videos

Updated: January 18, 2018 — 3:18 pm

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