Data science in R, phython , SAS and spark

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About the program

What are the learning objectives?
data2businessinsights’s Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures,Advanced analytics, Matplotlib, Excel analytics functions, Hypothesis testing, Zookeeper, Kafka interfaces. These skills will help you prepare for the role of a Data Scientist.

The program provides access to high-quality eLearning content, simulation exams, a community moderated by experts, and other resources that ensure you follow the optimal path to your dream role of data scientist.

Recommended Learning Path:

Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.

Tools & Technologies Covered

Advanced Analytics Tools :
Numpy, Pandas , tensorflow , Scipy and Spark
Plotly, matplotlib, seaborn, sci-kit learn,
statsmodel


Programming Lanuguages :
R, Python, SAS

Course Curriculum

Total learning: 41 lessons Time: 10 week
  • Data science in R, python , SAS and spark Training session  4 lessons 0/4

    • Data Science Introduction 30 minute
    • Introduction to Machine Learning 30 minute
    • Descriptive Statistics – Measures of Central Tendency 30 minute
    • Measures of Spread 38 minute
  • Python Programming  11 lessons 0/11

    • Python Introduction, data types, data structures, Pandas assignments 30 minute
    • Pandas Hands on & Assignments 30 minute
    • missing values, merge, join, concat & data frame Assignments 30 minute
    • Data Visualization 30 minute
    • Python Programming Part 1 30 minute
    • Numpy and Pandas 30 minute
    • Reshaping the data 30 minute
    • Handling Missing Values 30 minute
    • Data Visualization in Python – Introduction 30 minute
    • Matplotlib : Data Visualization 30 minute
    • Seaborn: Data Visualization 30 minute
  • Inferential Statistics  7 lessons 0/7

    • Probability 30 minute
    • Probability Distribution
    • Introduction to Inferential Statistics and Hypothesis Testing 30 minute
    • Hypothesis Testing Part 2 30 minute
    • Single Sample Hypothesis z test 30 minute
    • One and Two Sample T tests 30 minute
    • Hypothesis Testing in R 30 minute
  • Anova (Analsysis of Variance) Testing  4 lessons 0/4

    • Introduction to Anova Testing 30 minute
    • One way Anova – Part 2 30 minute
    • Two way Anova without replication
    • Two way Anova with replication
  • R Programming  5 lessons 0/5

    • Introduction to R 30 minute
    • Basic Data Types in R 30 minute
    • Data Structures in R 30 minute
    • Factor 30 minute
    • Outlier 30 minute
  • Supervised Machine Learning  6 lessons 0/6

    • linear Regression Part 1 30 minute
    • linear regression demo Part 2 30 minute
    • logistic regression part 1 30 minute
    • logistic regression class room Part 2 30 minute
    • Decision Tree Introduction part 1 30 minute
    • Decision Tree Part 2 30 minute
  • Unsupervised Machine Learning   3 lessons 0/3

    • Unsupervised Machine Learning – KMeans – 30 minute
    • Hierarchical Clustering 30 minute
    • Market Basket Analysis 30 minute
  • Machine Learning Questions  2 lessons 0/2

    • Machine learning Questions 30 minute
    • Data Science Quiz -Part 1 10 minute
Instructors

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