Data Science Training in Hyderabad | Best 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)
 Spark Vs Hadoop
 Spark Architecture
 Distributed Computing Advantages
 Rdd Concept
 Spark Mllib: Data Types, Algorithms, And Utilities

Share this

Share on FacebookPlus on Google+