The responsibility of Data Analysts can vary across industries and companies. Data Analysts utilizes the data to draw meaningful insights and solve problems. Data analysts can have a background in Mathematics,Statistics and skills include SQL, R or SAS, Python, Statistical analysis, Data analysis & Data Modeling.

About the course

- As part of the Data Analyst Program, we are going to teach on the Fundamentals of SQL,R, Python and application of different Mathematical and Statistical techniques, Data Visualization and Few Machine Learning algorithms
- Responsibilities: Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.
- Eligibility : MS/M.Tech/B.E/B.Tech/M.C.A/M.B.A/M.sc in Science/M.com
- Experience : 0-6 Years
- Duration of the Course : 100 Hours (3 Months)

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Course Details

Tools/Languages : SQL, R, Python, Excel, Minitab

Concepts of SQL |

R Programming for Data Science |

Python for Data Science |

Introduction of Descriptive Statistics |

About Qualitative & Quantitative Variables |

About Percentiles, Box Plots and Outlier detection |

Data Visualization (Pie Chart, Bar Chart, Line Chart, Scatter Plot) |

Introduction on Probability & Bayes Theorem & Examples |

Introduction on Inferential Statistics |

About Random Variables and Importance |

About Binomail, Bernouli and Poisson Random Variables and Examples |

Mean and Expected Values |

About Central Limit Theorem |

About Normal Distributions and Histogram & Examples |

Sampling Distribution Methods & Examples |

About Z,T,Chi-Square Distributions |

Construction of Confidence Interval for Population Mean & Problems |

Construction of Confidence Interval for Population Variance & Problems |

Introduction to Hypothesis Testing & Type 1 Errors & Type 2 Errors |

Hypothesis Testing for Population Mean for one Sample & Two Samples |

Fisher Distribution |

Hypothesis Testing for Population Variance for One sample & Two Samples |

Real Time Problems |

Introduction on Anova (Analysis of Variance) & Examples |

Introduction to Machine Learning |

Introduction Simple Linear Regression Models |

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 Classification Supervised Learning |

Logistic Regression & Problems |

Introduction to K-NN Classifier & Problems |

Introduction to Decision Trees |

Introduction to Unsupervised Learning |

Market Basket Analysis & Problems |

About Apriori/Support/Confidence/Lift & Problems |

Introduction to Cluster Analysis |

Agglomerative Hirarchical Clustering & K-Means Clustering & Problems |

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