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 conceptualise 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 is a combination of multiple fields and domains, including Artificial Intelligence (AI), data analytics, scientific methods, statistics, etc. Data science Courses in India are all about extracting and analyzing different forms of data using various tools and techniques to come up with robust solutions and information that help businesses make better decisions. The data science course in Hyderabad uses comprehensive machine-learning techniques to come up with compound models. Data science online course. People who work with and practice data science are known as data scientists. MindQ Systems offers the best Data Science course in Hyderabad with extensive training methods and techniques. Data science is one of the most in-demand courses across the globe, for its intuitive and remarkable data mining and resourcing capabilities from data lakes and databases. Data Science is capable of providing insightful solutions and outcomes that can greatly benefit large-scale companies and help them improvise and extend their services in a better and updated manner. The data science online course provided by us comes with a customized curriculum, taught by the best trainers in Hyderabad. You will also be rewarded with a course completion certificate to validate your skills and experience.  Data science online course Best Data Science Course In India

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

Module 1: Python

➢ 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

Module 2: Core Python

➢ 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

Module 3: Advance Python

➢ Oops
➢ Inheritance
➢ Polymorphism
➢ Encapsulation
➢ Abstraction
➢ Lambda Function
➢ Advance Python
➢ Map, Filter, Reduce
➢ Regular Expression
➢ Exception Handling
➢ Serialization
➢ REST API
➢ GIT / GIT HUB

Module 4:Database / Data Manipulation-Numpy

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

Module 5: Database / Data Manipulation-Pandas

➢ 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

Module 6:Database / Data Manipulation

Databases

➢ What is Database?
➢ Types of Databases?
➢ What is DBMS?
➢ What is RDBMS?
➢ History of RDBMS

Module7:SQL Database

➢ SQL Server / MySql
➢ CRUD Operation
➢ Select … Where
➢ Insert
➢ Update
➢ Delete
➢ Joins
➢ Primary & Foreign Keys
➢ Connectivity with
Python

Module8:NoSQL Database

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

Module9:Statistics

➢ 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

Module 10:Feature Engineering

Outlier Detection

➢ Standard Deviation
Method
➢ Inter Quartile Range
Method
➢ Z-Score Method
➢ Percentile Method

Exporatory Data Analysis

➢ Uni – Variate Analysis
➢ Bi – Variate Analysis
➢ Multi – Variate Analysis
➢ Matplotlib
➢ Seaborn

Encoding Techniques

➢ Pandas Dummies
➢ One Hot Encoding
➢ Label Encoding
➢ Ordinal Encoding
➢ Lambda with Apply
Function
➢ Lambda with Map
Function

Inferential Statistics

➢ Hypothesis
➢ Types of Hypothesis
➢ Null Hypothesis
➢ Alternative Hypothesis
➢ Chi-Square Test
➢ Anova Test
➢ T-Test
➢ Z-Test

Imbalance Dataset
➢ Under Sampling
➢ Over Sampling

Module 11: Visualization

Matplotlib

➢Bar Graph.
➢Pie Chart.
➢Box Plot.
➢Histogram.
➢Line Chart
➢Subplots
➢Scatter Plot.

Tableau

➢What is Tableau
➢Tableau Architecture
➢Server Components
➢Install Tableau
➢Data Connections to
➢Databases
➢Types of Filters
➢Groups in Tableau
➢Tableau Charts
➢Tableau Graphs

Seaborn

➢Count plot
➢Heatmap
➢Scatter plot
➢Pair plot
➢Violin Plot
➢Box plot
➢Strip Plot
➢Swarm Plot

Module 12:Machine Learning

Supervised Learning

Classification

➢Logistic Regression
➢Decision Tree
➢SVC – SVC
➢Naïve Bayes
➢KNN
➢Ensemble
➢Random Forest
➢Ada Boost
➢GradientBoost
➢XGBoost

Regression

➢Linear Regression
➢Multi Linear Reg
➢Polynomial Reg
➢Lasso Regression
➢Ridge Regression
➢Decision Tree
➢SVM — SVR
➢Ensemble Methods

Unsupervised Learning

Clustering

➢K-Means
➢C-Means
➢Hierarchical
➢Neural Network

Module 13:Machine Learning-Linear Regression

➢What is Correlation
➢What is Regression
➢What is Linear
Regression
➢Linear Regression
Overview
➢Simple Linear
Regression
➢Multi Linear Regression
➢Polynomial Regression
➢Related Concepts
➢Bias
➢Variance
➢Bias-Variance Tradeoff
➢Under Fitting Problem
➢Over Fitting Problem

➢What is Regularization
➢Types of Regularization
➢Lasso Regression
➢Ridge Regression

Mathematical Intuition

➢Linear Regression
➢Polynomial Regression
➢Lasso Regression
➢Ridge Regression

Regression / Evolution
Metrics

➢What is Actual Value
➢What is Predicted Value
➢What is Residual
➢R Squared (R^2)
➢Mean Squared Error
(MSE)
➢Root Mean Squared
➢Error (RMSE)
➢Mean Absolute Error
(MAE)

Module 14:Machine Learning Classification Algorithms

Decision Tree Classifier

➢What is Decision Tree?
➢Terminology of DT
➢Root Node
➢Splitting
➢Decision Node
➢Leaf Node
➢Pruning
➢Sub Algorithm of DT
➢CART Algorithm
➢ID3 Algorithm
➢Gini, Entropy,
Information Grain
➢Mathematical Intuition
➢Real World Data
➢Implementation

Naive Bayes Algorithm

➢What is Probability
➢Conditional Probability
➢What is Bayes Theorem
➢Naïve Bayes Algorithm
➢Types of Naïve Bayes

Logistic Regression

➢Logistic Regression
Overview
➢What is Sigmoid
Function
➢Mathematical Intuition
➢Implementation on real
world data

Support Vector Machines

➢Introduction to SVMs
➢SVC & SVR
➢SVM History
➢Vectors Overview
➢Decision Surfaces
➢Linear SVMs
➢The Kernel Trick
➢Non-Linear SVMs
➢The Kernel SVM

Module 15: Ensemble Learning • Introduction to
➢Ensemble Learning
➢Weak Learning?
➢Types of Ensemble
Learning
➢Boosting Algorithms
➢Ada Boost
➢GradientBoost
➢XGBoost
➢Implementation of
Ada Boost
➢GradientBoost
➢XGBoost Hyperparameter Tuning➢What is Hyperparameter?
➢Types of Hyperparameter
Tuning
➢Grid Search Tuning
➢ Randomise Search
➢Tuning Cross Validation➢What is Cross Validation?
➢Why we need Cross
Validation
➢Types of Cross Validation
➢Leave One Out Cross
Validation
➢Hold Out Cross
➢Validation Method
➢K-Fold Cross Validation
Method
➢Stratified Cross Validation
Method
Module 16: Clustering & Time Series

Clustering

➢What is Clustering
➢Types of Clustering
Methods
➢Partitioning Clustering
➢Hierarchical Clustering
➢Density Based
Clustering
➢K-Means Clustering
algorithm
➢Implement K-Means
➢Hierarchical Clustering
Algorithm
➢Implement Hierarchical
Clustering

Image Processing using
Opencv

➢Image to Numpy Array
➢Grayscale Image
➢Image Resize
➢Image Events
➢Image Flip
➢Image crop

Time Series Analysis

➢Time Series data ?
➢Format Time Series data
➢components of Time
➢Series data
➢Time Series scenarios
➢Time Series Model
➢Selection
➢Time Series Model for
Forecast
➢What is ARIMA Model ?
➢Implementation of
➢ARIMA model

Module 17:Deep Learning

Deep Learning

➢What is Deep Learning
➢Machine Learning VS Deep Learning
➢Biological Neural Network
➢Deep Learning Application
➢Artificial Neural Network(ANN)
➢Convolutional Neural Network(CNN)
➢Recurrent Neural Network(RNN)

Tensor Flow

➢What is TensorFlow
➢What are Tensors
➢Tensor Graph
➢TensorFlow Perceptron
➢Single Layer
➢Perceptron
➢Hidden Layer
➢Perceptron
➢Multi-Layer Perceptron

Activation Function
➢What is Activation
Function
➢Types of Activation
Function
➢What is Optimiser
➢What is Loss Function

Module 19:Deep Learning-AI

Artificial Neural Network

➢The Detailed ANN
➢How do ANNs work
➢Gradient Descent
➢Stochastic Gradient
Descent
➢Forward Propagation
➢Backpropagation
➢limitations of a Single
➢Perceptron
➢Neural Networks in Detail
Understand
➢Backpropagation

Computer Vision (Using CNN)

➢Convolutional Neural
Network
➢Why CNN
➢Application on CNN
➢Convolutional Layers
➢Pooling Layers
➢Batch Normalization
Layers
➢Dropout Layers

Natural Language Processing

➢Natural Language
Processing?
➢Tokenization
➢Stemming
➢Lemmatization
➢Stop Words
➢Phrase Matching
➢Vocabulary
➢Part of Speech Tagging
➢Named Entity
➢Recognition
➢Part of Speech Tagging
➢Named Entity
➢Recognition
➢Sentence Segmentation
➢Sentiment Analysis
with NLTK
➢Text Classification
➢Recurrent Neural
Network

Module 18: Additional Concepts

Hadoop

➢Hadoop Introduction
➢Hadoop Architecture
➢Hadoop Eco–System
➢HDFS
➢Hadoop Coursera
➢Py-Spark
➢Hive

AWS

➢Cloud Computing
➢AWS Introduction
➢Creating AWS Account
➢EC2 Details
➢Deploying Flask & ML
Model

Flask

➢Flask Introduction
➢Flask Application
➢Flask URL
➢Templates
➢Merge the ML Model

Kafka

➢What is Message
Service
➢Kafka Introduction
➢Kafka Architecture
➢Implementation with
Python

AGILE SCRUM METHODOLOGY

➢Agile Introduction
➢Advantages of Agile
➢Scrum Introduction
➢Scrum Process
➢Scrum Terminology