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/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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