# Data Analyst

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.

• 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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