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# Data Science Training in Hyderabad | Data Scientist Training India

Data Science is considered as the new arena, which is the most emerging technology that can easily enhance the Organizational growth. Data Administration and Management is being the biggest challenges that can face real time challenges in the explosion of happening these days.

What is Data Science?

Data Science is the software library framework which allows for the distributing processing large sets of data across a cluster of computers by using simple programming tools. It can easily scale up from a single server to thousands of machines in an easy manner.

Prerequisites and Requirements of Data Scientist

• There are 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

Duration
• 40 to 50 Hours

Course Content:

Introduction to Data Science
What is Data Science?
Role of Data Science
Scope of Data Science
1. Descriptive and Inferential Statistics
Samples and Populations
Sample Statistics
Estimations of Population Parameters
Random and Non-random Sampling
Sampling Distributions
The Central limit Theorem
Degree of Freedom
Percentiles and Quartiles
Measures of Central Tendency
Mean
Median
Mode
Measures of Variability/Dispersions
Range
IQR
Variance
Standard Deviation
Distributions
Normal Distributions
Binomial Distribution
Probability Distribution
Events, Sample Space and Probabilities
Conditional Probabilities
Independence of Events
Bayes’ Theorem
Random Variable
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
Similarity Metrics
Euclidean Distance
Jaccard Distance
Cosine Similarity
Graphical Representation and summaries
2. Data Exploration
Variable Identification
Uni-variate Analysis
Bi-variate Analysis
Missing Values Treatment
Imputation
Deletion
Prediction
Outlier Detection
Deletion
Binning and Transformation
Feature Engineering
Variable transformation
Variable / Feature creation
Dimensionality Reduction
Missing Values
Low Variance
High Collinearity
PCA
Factor Analysis
Principal Component Analysis
Data Summaries Using Stats and plots
Covariance, Correlation, and Distances
Correlation vs Causation
3. Machine Learning: Introduction and Concepts
Differentiating algorithmic and model based frameworks
Supervised Learning with Regression and Classification
Model Validation Approaches
Training Set
Validation Set
Test Set
Cross-Validation
Regression Algorithms
Linear Regression
Ordinary Least Squares
Ridge Regression
Lasso Regression
Unsupervised Learning
Clustering
Hierarchical (Agglomerative) Clustering
Non-Hierarchical Clustering: The k-Means Algorithm
Recommender Engines:
Collaborative Filtering Recommenders
Content Based Recommenders
4. R-Analytical Tool (Data Mining / Machine Learning)
Basic Data Types
R Data Structures
Vectors
Matrix
Data Frames
List
R Functions
Predictive Modeling Project based on R
Classification Model Attention:ing Project based on R
Clustering Project based on R
Association Mining Project based on R
R Visualization Packages
Machine Learning Packages in R
5. Python Scientific Libraries for Machine Learning
Scikit-Learn
Numpy
Scipy
Pandas
Matplotlib
Rmsc
R/Square
K Nearest Neighbors Regression & Classification
Classification
Logistic Regression
Naive Bayes
Classifier Threshold And Interpretation
Confusion Matrix-Error Measurement
Roc Curve
Accuracy, Precision, Recall
Measuring Sensitivity And Specificity
Regression And Classification Trees
Decision 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
Forecasting (Time-Series Modeling )
Trend And Seasonal Analysis
Different Smoothing Techniques
Arima Modeling
6. Spark Mllib (Scalable Machine Learning)