### Data Science Online Training in Hyderabad

In this **Data science online training in Hyderabad** 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.

Faculty : Real Time Expert | Duration : 70 to 90 hrs | Material : Yes | Price : Rs. 90,000/-

Itabhyas online training is the **Best Data Science Online Training in Hyderabad, Bengaluru, Chennai, India.**

**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 Hyderbad** 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?**

It Abhyas 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 prerequisites. 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.

**Data Science Online Training** batches will start every week. Make a call on +91-9030403937 or send a mail to info@itabhyas.com For **Data Science Online Training in Hyderabad, Bengaluru, Chennai, India**.

**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 Population**s**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<**/h2>

- Sentiment Analysis
- Use Behaviour Analysis
- Topic Categorization
- Topic Ranking

**Recommender Engines:**

- Collaborative Filtering Recommenders
- Content Based Recommenders

**Data Science Techniques Implementation by R – LanguageIntroduction 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

- who is a trainer ?

IT Abhyas trainers are working professionals from the Industry and have 10 yrs of relevant experience. - Will i ask for Demo session?

yes , we r conducting the demo sessions when u need. - How i will practice ?

We will provide a software to do the practice.In case you come across any doubt, we have a 24*7 support team they will assist you. - If I miss the session ?

Any situation you are not attend the session we will provide the Recorded session. - What about the course Material?

We are ready to provide the course material. - will i get the videos of course?

yes , you get the videos after completion of daily session.that access for life time. - Will i enroll now take a sessions after?

yes you will join u take a sessions later. - If i have any queries ?

you will send a mail or give a call to support team.