Best Data Science Course Training In Hyderabad
The various stages of the Data Science Lifecycle are explored in the trajectory of this course. This Best Data Science Course in Hyderabad begins with an introduction to Statistics, Probability, Python, and R programming. The student will then conceptualize Data Preparation, Data Cleansing, Exploratory Data Analysis, and Data Mining (Supervised and Unsupervised). Comprehend the theory behind Feature Engineering, Feature Extraction, and Feature Selection. Participants will also learn to perform Data Mining(Supervised) with Linear Regression and Predictive Modeling with Multiple Linear Regression Techniques. Data Mining Unsupervised using Clustering, Dimension Reduction, and Association Rules are also dealt with in detail. Best Data Science Course In India
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Data Science Course In Hyderabad
Testbug Solutions provides the Data Science Course in Hyderabad to help you become an industry-competent data scientist. The course allows you to learn from scratch and according to the industrial requirements. The Data Science Course has been in high demand in recent years, and our course is designed to provide you with current knowledge in the field. Our approach to practical and theoretical education provides a richer learning experience. Our Data Science institute in Hyderabad is cost-effective and even guarantees an interview at top MNCs and FinTech Startups. Best Data Science Course In India.
Data Science Overview
Data Science Course in Hyderabad Highlights
- Learn the essential fundamentals of Data science.
- High-end training from experts with 12+ years of experience.
- Tailored course curriculum
- Data analysis test cases for practical exposure
- Mock interviews and assessments.
- Verified Data Science course completion certificate.
- 100% placement assistance
- Resume Preparation
- Build recognized credibility in your profile. Data Science Course In Hyderabad
Course
Curruculum
➢ Introduction to
➢ Python
➢ History of Python
➢ Python Installation
➢ IDE’s – Pycharm
➢ Identifiers
➢ Statements
➢ Comments
➢ Variables
➢ Basic Python
➢ Types of Data Types
➢ Integers
➢ Float
➢ Complex
➢ Boolean
➢ String
➢ Operators
➢ Memory ManagIntroduction to
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➢ Conditional Statements
➢ Iterative Statements
➢ Interruptive Statements
➢ List
➢ Tuple
➢ Set
➢ Dictionary
➢ Functions
➢ Core Python
➢ Arguments Type
➢ Nested Function
➢ Closure Property
➢ Recursion
➢ Files
➢ Text Files
➢ CSV Files
➢ PDF Files
➢ Oops
➢ Inheritance
➢ Polymorphism
➢ Encapsulation
➢ Abstraction
➢ Lambda Function
➢ Advance Python
➢ Map, Filter, Reduce
➢ Regular Expression
➢ Exception Handling
➢ Serialization
➢ REST API
➢ GIT / GIT HUB
Numpy
➢ What is Numpy
➢ History of Numpy
➢ What is Ndarray
➢ Creating Numpy Array
➢ Array Function
➢ Creating Numpy Array
➢ Array Attributes
➢ Creating Multi-Dimensional
➢ Array
➢ Extracting Data from Arrays
➢ Numpy
➢ Using Indexing
➢ Using Slicing
➢ Boolean Indexing
➢ Random Indexing
➢ Resizing & Reshaping
➢ Transpose
➢ Vector multiplication
➢ Array Attributes
➢ Array Operations
➢ Broadcasting Rules
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➢ What is Data Manipulation
➢ What is Pandas
➢ History of Pandas
➢ What is Data Structure
➢ Pandas Data Structure
➢ Series
➢ DataFrame
➢ Creating Series
➢ Creating DataFrame
➢ Extracting Data
➢ Manipulation of Data
➢ Inserting Columns & Rows
➢ Changing Columns & Rows
➢ Pandas
➢Deleting column /
rows Re-indexing
➢Options Customization
➢Indexing & Selecting
➢Date Functionality
➢Identifying Outlier
➢Replace NaN using
➢Deleting using Drop,
➢Dropna
➢Concatenate and Merge
➢Groupby, Pivot Table
and Cross Tab
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Databases
➢ What is Database?
➢ Types of Databases?
➢ What is DBMS?
➢ What is RDBMS?
➢ History of RDBMS
➢ SQL Server / MySql
➢ CRUD Operation
➢ Select … Where
➢ Insert
➢ Update
➢ Delete
➢ Joins
➢ Primary & Foreign Keys
➢ Connectivity with
Python
MongoDB
➢ What is NoSQL DB
➢ NoSQL DB and SQL DB
➢ History MongoDB
➢ Features NoSQL
➢ Databases
➢ Create & Drop Database
➢ Create & Drop
➢ Collection
➢ Data Types
➢ Create, Insert, Update,
➢ Delete
➢ Query Document
➢ What is Statistics
➢ Types of Statistics
➢ What is Population
➢ What is Sample
➢ Different Sampling
  Techniques
➢ Statistics Terminology
Descriptive Statistics
➢Central Tendency
Measure
➢Measure of Variability
➢Dispersion Measures
➢Data Distributions
Inferential Statistics
➢ Hypothesis
➢ Types of Hypothesis
➢ Null Hypothesis
➢ Alternative Hypothesis
➢ Chi-Square Test
➢ Anova Test
➢ T-Test
➢ Z-Test
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➢ Introduction to Various Frameworks.
➢ Data Driven Tests Using POI
➢ Reading, Writing data into Excel.
➢ Data base Connection (JDBC).
➢ Reading , Writing Data Into MYSQL, SQL.
➢ Page Object Model Framework (POM).
➢ Writing Scripts Using POM.
-  ➢ Configuring Test Suits.
➢ Passing Parameters to Tests.
➢ Parallel Test Execution Capability.
➢ Re-run Failed Test Scripts.
➢ Attributes of @Test.
➢ Running TestNG Suite from Command Prompt.
➢ What is Version Control System.
➢ What Is GitHub.
➢ Git Commands.
➢ Pushing our project into GitHub.
➢ Git vs Github.
