Data Scientist

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. The Data Scientist role is a branch of several traditional technical roles, including Mathematician, Statistician and Computer professional as well as interest in gaining the knowledge on the Business problems.

About the course
  • A data scientist is a professional responsible for collecting, analyzing and interpreting/Predicting on extremely large amounts of data. Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals.
  • As part of the Data Science Program, we are going to teach on Descriptive & Inferential Statistics , R Programming for Data Science , Basic concepts in Regression Analysis, Machine Learning Algorithms & Python for Data Science
  • Eligibility : MS/M.Tech/B.E/B.Tech/M.C.A/M.B.A/ in Science/
  • Experience : 3 Years & Above
  • Duration of the Course : 120 Hours (4 Months)
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Course Details

Tools/Languages :  R, Python, Excel, Minitab

Introduction to Data science
Introduction to Descriptive and Inferential Statistics
Turning Data into Information using Data Visualization, Measures of Central Tendency , Measures of Variables etc.
Concepts of Correlation and Covariance  & Examples
Introduction on Probability & Bayes Theorem
About Probability Distributions 
About Discrete and Continuous Random Variables
Bernouli ,Binomial & Poison Distributions And Mean, Expected Values
About sampling Distributions & Central Limit Theorem
Importance of Normal Distribution & Examples
Introduction to  Confidence Intervals
Construction of Confidence Interval to Estimate Population Mean & Variance 
Introduction on Test of Hypothesis or Hypothesis Testing
Hypothesis Testing for Mean & Variance for one Sample & Problems
Hypothesis Testing for Mean & Variance for two Samples & Problems
Introduction to ANOVA (Analysis of Variance)
ANOVA Assumptions & One-way  & Two-way ANOVA
Multiple Comparisons  using (Tukey & Dunnet)  Methods & Problems
Introduction to R Programming 
Concepts of  R for Data Science
Introduction to Regression Analysis
About Simple Linear Regression Model
Least Square Estimators of Parameters
Hypothesis Testing of Slope and Intercepts
Coefficient of Determination and Problems
Introduction to Multiple Linear Regression Model
Estimation of Model Parameters
Hypothesis Testing in MLR  & Multicollinearity & Problems
Variable Selection and Model Building
Forward Selection, Backward Elimination, Stepwise Regression & Problems
Polynomial Regression & Problems
Introduction to Applied Multivariate Analysis
Multivariate Normal & Outlier Detection
Eigen Values & Eigen Vectors & Spectral Decomposition
Distribution of Sample Mean Vector and Inference for Correlations
Introduction to Principal Component Analysis (PCA) & Problems
Introduction to Machine Learning
Introduction to Classification Algorithms
About Bayes Law & Naive Bayes  Algorithm & Problems
K-NN Classifier (K-Nearest Neighbor Method) & Problems
Logistic Regression & Problems
Introduction to Decision Trees
Classification Trees & Problems
Introduction to Random Forest
Bagging , Bootstrap & Ensemble Methods  & Problems
Introduction to Support Vector Machine
Maximum Marginal Classifier, Support Vector Classifier Problems
Kernel Trick &  SVM with more than two classes & Problems
Introduction to Unsupervised Learning
Introduction to Cluster Analysis
 Agglomerative Hirarchical Clustering & K-Means Clustering & Problems
Introduction to Association Rules
About Market Basket Analysis
About Apriori/Support/Confidence/Lift  & Problems
Introduction to Regression Shrinkage Methods
Ridge Regression & Lasso Regression & Problems
Introduction to Artificial Neural Networks & Problems
Regression Trees & Problems
Introduction to Python
Concepts of Python for Data Science
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Updated Thu, 29-Oct-2020
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