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DATA SCIENCE ONLINE TRAINING IN HYDERABAD, INDIA


Course name : Data Science Online Training

24*7 technical support

Duration : 60 hours

faculty : Realtime experience


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About online training expert trainers

Online trainings expert prides itself on ensuring that our online trainers are real time experts.only the online training company deliver online training programs to our valued candidates.

As part of online trainings expert continuous improvement in online trainings. Each trainer is regularly assessed to candidates given the best quality of training every time.

All our trainers are skilled trainers and addition have the real time experience with proven track record in online trainings. This experience qualifies them as a specialist in their online training delivery in their course.

Why Data Science Online Training Only with online trainings expert?

1. We just do not teach you technology. We Share Our Real time implementation expertise to gain practical skills and knowledge to face customers, implement better solutions further to enlighten your careers.
2. We just do not teach curriculum. We make sure to see that you are up to the industry expectations by giving real time scenarios, providing case studies, and giving project oriented examples. We will also provide well designed Lab Manuals, Study Materials, and Practice Exercises for quick learning process.
3. We just do not make developers or consultants or managers. We make full pledged competitive IT Professionals.

Our Data Science online Training Highlights

Technical Support
Interview Questions
Sample Resumes



What is Data Science?

In this Data science online training you will understand all basics to advanced statistics and learn how to program in R & Python and how to use R & Python for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The data science online training in Hyderabad covers practical issues in statistical computing, which includes programming in R & Python, reading data into R & Python, accessing R packages & Python data science library and frameworks, writing R & Python functions, debugging, profiling R & Python code. Topics in statistical data analysis will provide working examples.

Who learn This Data Science course?

  • Non-IT Professionals
  • Developers
  • Non-BI Professionals
  • Data Analysts
  • Project Managers
  • Job seekers
  • Graduates

How I Execute the practicals?

career It Trainings provide data science On line training related software and tools.

What are the prerequisites for Data Science On line training?

The Data science Online training course has no pre-requisites. No prior knowledge of Statistics, the language of R, Python or analytic techniques is required. This course covers from basic to advanced Statistics and Machine Learning Techniques.

Introduction to Data Science

  • Introduction to Data Science, Tables,Database,ETL, EDW and Data Mining
  • What is Data Science?
  • Popular Tools
  • Role of Data Scientist
  • Analytics Methodology

Descriptive and Inferential Statistics

Statistics is concerned with the scientific method by which information is collected, organized, analyzed and interpreted for the purpose of description and decision-making.

There are two subdivisions of statistical method

Descriptive Statistics - It deals with the presentation of numerical facts, or data, in either tables or graphs form, and with the methodology of analyzing the data.


Inferential Statistics - It involves techniques for making inferences about the whole population on the basis of observations obtained from samples.


Samples and Populations

  • Sample Statistics
  • Estimations of Population Parameters
  • Random and Non-random Sampling
  • Sampling Distributions
  • Degree of Freedom
  • The Central limit Theorem

Percentiles and Quartiles

Measures of Central Tendency

  • Mean
  • Median
  • Mode

Measures of Variability/Dispersions

  • Range
  • IQR
  • Variance
  • Standard Deviation

Skewness and Kurtosis

Probability Distributions

  • Events, Sample Space and Probabilities
  • Conditional Probabilities
  • Independence of Events
  • Baye's Theorem
  • Random Variable
  • The Normal Distributions
  • Confidence Intervals
  • Hypothesis Testing
  • Null Hypothesis
  • The Significance Level
  • p-value
  • Type I and Type II Errors

Inferential Test Metrics

  • t test
  • f test
  • Z test
  • Chi square test
  • Student test

The Comparison of Two Populations

Analysis of Variance

  • ANOVA Computations
  • Two-way ANOVA

Data Exploration and Dimension Reduction

  • Data Summaries
  • Covariance, Correlation, and Distances
  • Missing Values Handling
  • Outliers Handling
  • Principal Component Analysis
  • Exploratory Factor Analysis

Machine Learning:

Introduction and Concepts : Differentiating algorithmic and model based frameworks

Regression

  • Ordinary Least Squares
  • Ridge Regression
  • Lasso Regression
  • K Nearest Neighbours Regression & Classification

Supervised Learning with Regression and Classification

  • Bias-Variance Dichotomy
  • Model Validation Approaches
  • Training Set
  • Validation Set
  • Test Set
  • Cross-Validation
  • Logistic Regression
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis

Regression and Classification Trees

  • Recursive Portioning
  • Impurity Measures (Entropy and Gini Index)
  • Pruning the Tree

Support Vector Machines

Ensemble Methods

  • Bagging (Parallel Ensemble) - Random Forest
  • Boosting (Sequential Ensemble) - Gradient Boosting

Neural Networks

  • Structure of Neural Network
  • Hidden Layers and Neurons
  • Weights and Transfer Function

Deep learning

  • Integrated best features of both Machine Learning and NN
  • Forecasting ( Time-Series Modelling )

    • Trend and Seasonal Analysis
    • Different Smoothing Techniques
    • ARIMA Modelling
    • ETS Modelling

    Unsupervised Learning

    Clustering

    • Hierarchical (Agglomerative) Clustering
    • Non-Hierarchical Clustering: The k-Means Algorithm

    Associative Rule Mining

    • Aprori Algorithms
    • Frequent Item-sets
    • Support
    • Confidence
    • Lift Ratio
    • Discovering Association Rules

    Text Mining

    • Sentiment Analysis
    • Use Behaviour Analysis
    • Topic Categorization
    • Topic Ranking

    Recommender Engines:

    • Collaborative Filtering Recommenders
    • Content Based Recommenders

    Data Science Techniques Implementation by R - Language

    Introduction to R Foundation

    • Software Installation on Various Operating Systems
    • Introduction to Real Time Applications
    • Introduction to Popular Packages

    R-Analytical Tool (Data Mining / Machine Learning)

    • Basic Data Types
    • R Data Structures
    • Vectors
    • Matrix
    • List
    • Data Frames
    • R Functions
    • Predictive Modelling Project based on R
    • Classification Modelling Project based on R
    • Clustering Project based on R
    • Association Mining Project based on R
    • R Visualization Packages
    • Machine Learning Packages in R

    Python - Getting Started

    • Installing Python on Windows
    • Installing Python on Mac and Linux
    • Introduction to Editors
    • Installing PyCharm and Sublime Editors

    Python Basics

    • Numbers and Math in Python
    • Variable and Inputs
    • Built in Modules and Functions
    • Save and Run Python Files
    • Strings
    • Python List
    • Python slices and slicing

    Python Scientific Libraries for Machine Learning

    • Scikit-Learn
    • Numpy
    • Scipy
    • Pandas
    • Matplotlib

    Introduction to Data Visualization

    • Introduction to Data Science and Visualization Tools in Python
    • Installing and Setting up iPython Notebook
    • Installing Anaconda and Panda
    • Setting Up Environment

    Learning Numpy

    • Creating Arrays
    • Using Arrays and Scalars
    • Indexing Arrays
    • Array Transposition
    • Universal Array Function
    • Array Processing
    • Array Input and Ouput

    Working with Panda

    • Series
    • Data Frames
    • Index Objects
    • Reindex
    • Drop Entry
    • Selecting Entries
    • Data Alignment
    • Rank and Sort
    • Summary Statistics
    • Missing Data
    • Index Hierarchy

    Working with Data Part 1

    • Reading and Writing Text Files
    • Json with Python
    • HTML with Python
    • Microsoft Excel Files with Python

    Working with Data Part 2

    • Merge, Merge on Index and Concatenate
    • Combining Data Frames
    • Reshaping and Pivoting
    • Duplicating Data Frames
    • Mapping, Replacing, Rename Index and Binning
    • Outliers and Permutations

    Working with Data Part 3

    • Group by on Data Frames
    • Group by on Dist Series
    • Aggregation
    • Splitting, Applying and Combining
    • Cross Tabulation

    Working with Visualization

    • Installing Seaborn
    • Histograms
    • Kernel Density and Estimate Plots
    • Combining Plot Styles
    • Box and Violin Plots
    • Regression Plots
    • Box and Violin Plots
    • Heat Maps and ClusteredMatrices
    • Example Projects-15

    Machine Learning Language

    • Introduction
    • Linear Regression
    • Logistic Regression
    • Multi Class Classification - Logistic Regression
    • Multi Class Classification - Nearest Neighbor
    • Vector Machines
    • Na?ve Bayes Theory

    Prescriptive analytics ( Optimization Techniques )

    • Introduction
    • Analytics through designed experiments
    • Analytics through Active learning
    • Analytics through Reinforcement learning

    Data Science based Projects

    • Cover couple of Real-Time Analytics Projects based on R Script and Python Scientific Libraries.

    SPARK MLlib (Scalable Machine Learning)

    • RDD Concept
    • Spark MLlib: Data Types, Algorithms, and Utilities


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