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WOOCS 2.1.5.4
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    Course Syllabus

    Day-1: Topic name?  Introduction to data.

    Description: (what student can learn after Day 1?) This unit will focus more on collecting, analyzing and visualizing the data as well as making data base decisions. Understanding population of interest and samples in data.

    Day-2: Topic name? Data basics ,observational studies and experiments

    Description: (what student can learn after Day 2?)  Students will learn about observations, variables, and data matrices, types of variables and relationship between variables, Correlation and causation.

    Day-3: Topic name? Sampling methods

    Description: (what student can learn after Day 3?) Students will learn about Simple random sampling, Stratified sampling and clustering methods.

    Day-4: Topic name? Visualizing numerical data, measures of center and measure of spread.

    Description: (what student can learn after Day 4?) Discuss on Scatter plots for paired data and other visualizations for describing distributions of numerical variables. Measures of center covering topics such as Skewness and modality.

    Day-5: Topic name? Robust statistics and transforming data.

    Description: (what student can learn after Day 5?) Students will learn about mean, median and mode. Robust measures of center and spread along with distribution of data methods such as histograms and normal distribution.

    Day-6: Topic name? Statistical inference

    Description: (what student can learn after Day 6?) In this hour students will learn the foundation of inference and get introduced to topics such as Central limit theorem.

    Day-7: Topic name? Continuation of Statistical inference

    Description: (what student can learn after Day 7?) Talking about topics such as Hypothesis testing for mean, Decision errors, Significance vs Confidence level and Statistical vs practical significance.

    Day-8: Topic name? Third session on Statistical inference.

    Description: (what student can learn after Day 8?) In this week we will talk about the t-distribution, anova and comparing means as well as simulation based methods for creating a confidence interval. End this session with bootstrapping.

    Day-9: Topic name? Linear regression

    Description: (what student can learn after Day 9?)  Linear models can be used to or evaluate whether there is a linear relationship between two numerical variables.

    Day-10: Topic name? More about linear regression 

    Description: (what student can learn after Day 10?) We will look at outliers, inference in linear regression and variability partitioning.

    Day-11: Topic name? Multiple regression

    Description: (what student can learn after Day 11?)  We will explore multiple regression, which allows us to model numerical response variables using multiple predictors (numerical and predictor).

    Day-12: Topic name? Understanding modelling.

    Description: (what student can learn after Day 12?) How to build a model , examples of model building.

    Day-13: Topic name? Different modelling techniques

    Description: (what student can learn after Day 13?) Understanding different modelling techniques, calculating correlation

    Day-14: Topic name? Validation phase in a model

    Description: (what student can learn after Day 14?)  Understanding about data cleaning, feature selection and build the classifier.

    Day-15: Topic name? Validation model continuation 

    Description: (what student can learn after Day 15?) Talking about ROC curve, sensitivity and specificity.

    Day-16: Topic name? Data mining –Unsupervised learning

    Description: (what student can learn after Day 16?)  Cluster analysis, Dimension reduction and who uses data mining techniques.

    Day-17: Topic name? Data mining –Unsupervised learning continuation 

    Description: (what student can learn after Day 17?) In this session we will talk about Principle component analysis, association rule and network analysis.

    Day-18: Topic name? Data mining – Unsupervised learning

    Description: (what student can learn after Day 18?) In this session we will give an introduction to unsupervised learning.

    Day-19: Topic name? Data mining –Unsupervised learning continuation

    Description: (what student can learn after Day 19?) We will talk about decision trees, random forest etc.

    Day-20: Topic name? Introduction to Machine learning

    Description: (what student can learn after Day 20?) Talking about Naïve bayes techniques and support vector machine.

    Day-21: Topic name? Get more into detail of machine learning

    Description: (what student can learn after Day 21?) Talk more about text learning, clustering and principle component analysis.

    Day-22: Topic name? Data visualization

    Description: (what student can learn after Day 22?) What is data visualization and why is it necessary in data science.

    Day-23: Topic name? Data visualization continuation

    Description: (what student can learn after Day 23?) Talking more about data visualization and various chart types.

    Day-24: Topic name? Hands on exercise on Tableau for data visualization Day 1

    Description: (what student can learn after Day 24?) Hands on exercise on tableau using real data set.

    Day-25: Topic name? Hands on exercise on Tableau for data visualization Day 2

    Description: (what student can learn after Day 25?) Hands on exercise on tableau.

    Day-26: Topic name? Big data and it’s usage.

    Description: (what student can learn after Day 26?) We will talk about big data, map reduce algorithm.

    Day-27: Topic name? Hands on exercise on R

    Description: (what student can learn after Day 27?) learn R programming from basics.

    Day-28: Topic name? R programming continuation

    Description: (what student can learn after Day 28?) Do real time exercise on R

    Day-29: Topic name? R programming continuation

    Description: (what student can learn after Day 29?) Learn more techniques on R and understanding its console.

    Day-30: Topic name? Introduction to Python

    Description: (what student can learn after Day 30?) Talk about Python and comparison of python and R.

    Project Classes

    Day-1: project content? Work hands on predictive modelling using the Titanic data set.

    Description: (what student can learn after Day 1?) Do exploratory data analysis on the titanic data set.

    Day-2: project content? Continuation on Titanic data set.

    Description: (what student can learn after Day 2?) Use various techniques for survival analysis using titanic data set.

    Day-3: project content? Chicago crime data

    Description: (what student can learn after Day 3?) Work on Chicago crime data on understanding crime patterns in Chicago city

    Day-4: project content? Continuation of Chicago crime data

    Description: (what student can learn after Day 4?)  Using best practices and various techniques of machine learning in understanding this real time project.

    Note: repeat this until last day of your Project. 

    How you help to the Student to build the resume? 

    Students should be able to leverage all the data science skills they have learned during the course and apply as per the job description. 

    How you will prepare them for the Interview?  Students after this course will have better understanding of the data. They will now have hands on experience on R and Tableau as well as brief idea on Python.

    Certificate Code

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    E mail : support@greatonlinetraining.com
    India : +91-9966956770
    USA : +1 (551) 226-6061
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