About Data Science

A Data Scientist is someone who is better at statistics than any software engineer and better at Software Engineering than any Statistician. They are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role in data analysis is becoming increasingly important as businesses rely more heavily on big data and data analytics to drive decision-making. offers Best Data Science Classroom Training in Hyderabad with Real-time experienced trainers
. Be a Certified Data Scientist in 90 Days by mastering R, Python, Machine Learning, Deep Learning, Statistics, Tableau, Hadoop and SQL.

Advanced Data Science Training in Hyderabad

A Data Science Course is the latest trending technology in the present technical world. is one of the Top Data Science Training Institute in Hyderabad with Placement assistance. We will provide the Data Science training with Real-time trainers, client case studies and live projects. Data Science course with will help to the Candidates to get in-depth knowledge of Data Science by laying the strong foundation for the career.


Module I - Introduction

  • Overview
  • Flow of course

Module II - Introduction to Data science

  • What is data science
  • Importance of data science
  • Demand for data science
  • Prerequisite to learn data science
  • Data science life cycle
  • Why Organizations hiring Data Scientist
  • Real Time Projects overview
  • Data science vs Business Intelligence
  • Terms to Remember in Data Science
  • Artificial Intelligence Overview
  • Overview of Real time Project on Artificial Intelligence
  • What is Data?
  • How Data is generated?
  • Data Storage Overview
  • Types of Data
  • Bascis of Software Life Cycle

Module III - Installations

  1. Installation of required softwares
  2. How to install Packages/ Libraries
  • Numpy
  • Pandas
  • Scipy
  • Scikit-Learn
  • Keras
  • Matplotlib
  • Seaborn
  • Cufflinks

Module IV - Python Programming for Data Science

  • Introduction to Python
  • Python 2 vs Python 3
  • Python Identifiers
  • Operators
  • Comments (Single and Multi lines)
  • Data Types
  • Advanced Data Structure
  • Different type of Loops
  • Conditions
  • Functions

Module V - Data exploration

  • Collecting data from different sources
  • Analyzing data
  • Data Preprocessing
  • Data Munging
  • Data Mining
  • Data Manipulation
  • Data Visualization
  • Feature Selection
  • Feature Scaling
  • Dimensionality Reduction Techniques

Module VI - Statistics

  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Analysis
  • Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution
  • Outlier Detection
  • Other Statistical fundamentals

Module VII - Machine Learning

  1. Introduction to Machine Learning
  2. Supervised learning
    Regression Models
  • Introduction to Linear Regression
  • Multiple Linear Regression
  • Ordinary Least Squares Method
  • Non Linear Models
  • Polynomial Regression
  • Support Vector Regressor
  • K-NN Regressor
  • Decision Tree Regressor
  • Random Forest Regressor
    Evaluation Metrics for Regression Models
  • R Square
  • Adjusted R Square
    Error Functions
  • Root Mean Square Error (RMSE)
  • Mean Absolute Error
    Error Functions
  • Root Mean Square Error (RMSE)
  • Mean Absolute Error
    Classification Models
  • Logistic Regression
  • Naive Bayes
  • Support Vector Classifier
  • K-NN Classifier
  • Decision Tree Classifier
  • Random Forest Classifier
    Evaluation Metrics for Classification Models
  • Accuracy
  • Precision
  • Recall
  • F1 score
  • ROC Curve

3.Unsupervised learning

    Clustering models
  • K-Mean Clustering
  • Hierarchial Clustering

4.Reinforcement learning

Module VIII - Introduction to Deep Learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans i.e., learn by example. The main concept in deep leaning algorithms is automating the extraction of representations (abstractions) from the data.

  1. Limitations of Machine Learning
  2. What is Deep Learning?
  3. Advantage of Deep Learning over Machine learning

Module IX - Understanding Neural Networks with TensorFlow

  1. How Deep Learning Works?
  2. Activation Functions
  3. Illustrate Perceptron
  4. Training a Perceptron
  5. Important Parameters of Perceptron
  6. Constants, Placeholders, Variables

Module X - Deep dive into Neural Networks with TensorFlow

  1. Understand limitations of a Single Perceptron
  2. Understand Neural Networks in Detail
  3. Illustrate Multi-Layer Perceptron
  4. Backpropagation

Module XI - Convolutional Neural Networks

  1. Introduction to CNNs
  2. CNNs Application
  3. Architecture of a CNN
  4. Convolution and Pooling layers in a CNN

Module XII - Recurrent Neural Networks

  1. Introduction to RNN Model
  2. Modelling sequences
  3. Training RNNs with Backpropagation
  4. Long Short-Term memory (LSTM)
  5. Recursive Neural Tensor Network Theory
  6. Recurrent Neural Network Model
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ServicesEssential Course PackageMid-Level Career BoosterMastery Career Package
Duration3 Months7 Months7 Months + 1 Year
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Best Artificial Intelligence Training in Hyderabad with 100% placement Assistance. Learn Data Science with Python, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Statistics and Tableau.


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