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

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

**Enroll today for 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 in Andhra Pradesh(India). @ present in Chicago (USA))** **

- Course Duration:60 Days
- Mon-Fri: 8 am to 10 am (IST)
- Mode of Training: Classes Room and Online
- Real-time Trainer
- After class, we will provide class recording for reference purpose
**Note***Everyday session recordings are also available

*మరో వారంలో కారు చెడిపోబోతోందని… మరి కొద్ది నెలల్లో క్యాన్సర్ వస్తుందని తెలిస్తే ఎన్ని అవస్థలు తప్పుతాయో, ఎంత జాగ్రత్త పడవచ్చో ఆలోచించండి. మీరు వస్తుండగానే మీ ముఖాన్ని గుర్తించి మీ తలుపులు తెరుచుకోవడం, వెనుక సీటులో హాయిగా పడుకుంటే వేల కిలోమీటర్లు డ్రైవర్ లేకుండా వేగంగా మీ కారు వెళ్లిపోవడం ఊహించడానికి చిత్రంగా ఉన్నా… అవన్నీ ఇప్పుడు నిజంగానే జరుగుతున్నాయి. అదంతా కృత్రిమ మేధ (ఆర్టిఫిషియల్ ఇంటెలిజెన్స్) మహిమే. ఈ ఆధునిక టెక్నాలజీ కొత్త కొత్త కోర్సులకు, భారీ సంఖ్యలో ఉద్యోగాల సృష్టికి కారణమవుతోంది. more* download pdf data science ai eenadu article

**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 Aesthetics
- 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: Winning 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**

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**VLR Training Address: **

Plot No 126/b,2nd floor, Street Number 4,

Addagutta Society, Near Jntuh,Pragarthi Nagar Road,

Kukatpally, Hyderabad, Telangana 500072**Name**: Data Science Online Training Jntu Hyderabad**Telephone**:9059868766**Opening hours**:7 Am to 9 Pm (IST)

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Will you teach unstructured data to structured and predictive analysis?

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