DATA SCIENCE ONLINE TRAINING KUKATPALLY – HYDERABAD

DATA SCIENCE ONLINE TRAINING

Data Science Online Training kukatpally Hyderabad provided by VLR Trainings. Data Science is that the study of wherever data comes from, what it represents and the way it is became a valuable resource in the creation of business and IT ways. More info  Wikipedia

DATA SCIENTIST

A data scientist is someone who is better at statistics than any software engineer and better at

Software engineering than any statistician.”

WHAT A DATA SCIENTIST DOES

Most data scientists in the industry have advanced degrees and training in statistics, math, and computer science. Their experience is a vast horizon that also extends to data visualization, data mining, and information management. It is fairly common for them to have previous experience in infrastructure design, cloud computing, and data warehousing.

SKILLS REQUIRED TO BECOME A DATA SCIENTIST

• Statistic and probability
• Algorithms
• Programming Languages (Java, Scala ,SQL, R, Phyton)
• Data mining
• Machine learning

Who should go for this course?

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

Data science Course Duration and details

Course Duration 90Days (3 months)

Course Fees 27000Rs

Only online training

Note* Everyday session recordings are also available

Data science Course Content

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Data Science Online Training VlrTraining Hyderabad

DATA SCIENCE

(I)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

(II) 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
(III) 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
(IV)  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

(V) 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

(VI)    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

(VII)    Spark and Map Reduce
1.    Introduction to Spark
2.    Spark integration with Jupyter
3.    Transformations and Actions
4.    Spark Data Frames
5.    Spark SQL
(VIII)    Building a Capstone Project

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