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.

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/ in Science/
  • 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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