# 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.

• 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/M.sc in Science/M.com
• 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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