Certified Data Scientist

Certified Data Scientist

547,500.00

 

At the end of the certified data scientist-R Language  training ,delegates will be technically competent in R programming language concepts such as data visualization, exploration; statistical concepts like linear & logistic regression, cluster analysis and forecasting.

Description

  • Certified Data Scientist – R Language Training Overview

     At the end of the certified data scientist-R Language  training ,delegates will be technically competent in R programming language concepts such as data visualization, exploration; statistical concepts like linear & logistic regression, cluster analysis and forecasting.JK Michaels  training in Business analytics & R is an ideal package for aspiring data analysts to gain expertise in data analytics.

 Certified Data Scientist – R Language Training

KEY FEATURES

BLENDED LEARNING =5 DAY INSTRUCTOR LED CLASSROOM TRAINING+2 MONTH ONLINE TRAINING

  • 30+ hours of training in R Language
  • 20 hours of real life industry project experience across 4 domains – Healthcare, Retail, Internet, Insurance
  • 9 hours of High Quality e-learning
  • 30 chapter-end quiz questions
  • Course taught via 5+ real life case studies
  • 90 days e-learning access
  • 2 months of training in R language
  • 4 Data Science Simulation Exams
  • Trained by Industry Experts having more than 5 years of experience in R language
  • Hands-on experience in predictive modeling on R
  • Become a Certified Data Scientist in R language
  • Continued Trainer support & guidance for clarifying doubts
  • Downloadable e-Books

Certified Data Scientist – R Language Training Objectives

JK Michaels training imparts immense learning benefits with a clear focus on the vital concepts of Business analytics and R. Participants at the end of our training in Business Analytics & R, will be able to:
  • Work on data exploration, data visualization and predictive modeling techniques with ease.
  • Gain fundamental knowledge on Analytics and how it assists in decision making.
  • Work with confidence in R language.
  • Understand and work on statistical concepts like linear & logistic regression, cluster analysis and forecasting.
  • Develop a structured approach to use statistical techniques and R language.
  • Perform sharp data analysis to make business decisions.

Course Outlines

Lesson 00 – Business Analytics Foundation With R Tools14:0
0.1 Business Analytics Foundation With R Tools 1.10
  • 0.2 Objectives1.34
  • 0.3 Analytics:57
  • 0.4 Places Where Analytics is Applied2:19
  • 0.5 Topics Covered2:25
  • 0.6 Topics Covered (contd.)2:11
  • 0.7 Career Path2:7
  • 0.8 Thank You
  • Lesson 01 – Introduction to Analytics29:48
    • 1.1 Introduction to Analyics1:45
    • 1.2 analytics vs analysis1:47
    • 1.3 What is Analytics3:17
    • 1.4 Popular Tools1:30
    • 1.5 Role of a Data Scientist1:58
    • 1.6 Data Analytics Methodology1:53
    • 1.7 Problem Definition3:28
    • 1.8 Summarizing Data2:21
    • 1.9 Data collection2:45
    • 1.10 Data Dictionary1:45
    • 1.11 Outlier Treatment2:55
    • 1.12 Quiz
  • Lesson 02 – Statistical Concepts And Their Application In Business139:30
    • 2.1 Statistical Concepts And Their Application In Business10:12
    • 2.2 Descriptive Statistics10:51
    • 2.3 Probability Theory22:38
    • 2.4 Tests of Significance22:23
    • 2.5 Non-parametric Testing8:11
    • 2.6 Quiz
  • Lesson 03 – Basic Analytic Techniques – Using R222:42
    • 3.1 Introduction6:16
    • 3.2 Data Exploration24:50
    • 3.3 Data Visualization2:59
    • 3.4 Pie Charts25:4
    • 3.5 Correlation8:29
    • 3.6 Analysis of variance11:13
    • 3.7 Chi-squared test9:50
    • 3.8 T-test29:15
    • 3.9 Summary1:55
    • 3.10 Quiz
  • Lesson 04 – Predictive Modelling Techniques383:18
    • 4.1 Predictive Modelling Techniques2:37
    • 4.2 Regression Analysis and Types of regression models3:48
    • 4.3 Linear Regression7:21
    • 4.4 Coefficient of determination R2:14
    • 4.5 How good is the model2:9
    • 4.6 How to find Liner regression equation3:49
    • 4.7 Commands to perform linear regression4:45
    • 4.8 Linear regression to predict sales8:14
    • 4.9 Case Study – Linear Regression8:17
    • 4.10 Case Study – Classification11:49
    • 4.11 Logistic regression5:39
    • 4.12 Example – Logistic regression in R8:2
    • 4.13 Logistic Regression Predicting recurrent visits to a web site9:51
    • 4.14 Cluster Analysis8:20
    • 4.15 Command to perform clustering in R6:16
    • 4.16 Hierarchical Clustering7:28
    • 4.17 Case Study – Implement K means and Hierarchical Clustering12:41
    • 4.18 Time Series3:39
    • 4.19 Cyclical versus seasonal analysis2:38
    • 4.20 Decomposition of Time Series4:24
    • 4.21 Caes Study- Time Series Analysis9:54
    • 4.22 Decomposing Non-Seasonal Time Series3:26
    • 4.23 Exponential Smoothing8:45
    • 4.24 Advantaged and Diadavantages of Exponential Smoothing1:51
    • 4.25 Exponential smoothing and forecasting in R2:50
    • 4.26 Example – Holt Winters14:58
    • 4.27 White Noise2:2
    • 4.28 Correlogram Analysis2:38
    • 4.29 Box-Jenkins forecasting Models12:22
    • 4.30 Case Study – Time Series Data using ARMA17:28
    • 4.31 Business Case20:50
    • 4.32 Summary1:52
    • 4.33 Thank You

Why is the certification most sought-after?

  • With a $14.4 billion revenue, the growing global business analytics market requires 250,000 certified analytics professionals to tackle the exploding big data across different verticals. Top companies like Amazon, Google, Oracle Corporation, IBM, Microsoft and Dell are looking for certified and skilled analysts to take on the data scenario. In addition, the Big 4 Audit firms – PWC, E & Y, Deloitte and KPMG are also hiring lot of data scientists.

What are the career benefits in-store for you?

  • Certified data scientists start with a higher salary and have better growth prospects than in their current role. Statistics from KDNuggets show that there is a tremendous salary increase by 50% and 13% in Asia and rest of the world respectively. According to Indeed.com, Certified R data scientists earn an average $97, 000 and are most sought-after in Fortune 500 companies. Being a data scientist opens up a plethora of career opportunities in almost every industry and vertical. Possessing this coveted skill makes it easier for an aspirant to switch to his/her industry or function of choice.

Who should do this course?

  • This course is meant for  students and professionals looking to enter the field of  data analytics and work as a data scientist or a business analyst professional.

EXAM & CERTIFICATION

  • What are the prerequisites for the certification?

    Though there are no specific pre-requisites, individuals with a strong bent in logical, reasoning and strong mathematical skills will find the course suitable.
    The American Association of Project management awards the certified data scientist credential after successful pass on an online exam and successful completion of a project.

What are the projects covered to get certified and their benefits?

  • JK Michaels training offers participants the opportunity to work on real-time, industry relevant, hands-on projects in the following domains. This provides an insight on the working of a real-life model in each industry. Few examples of the projects that will probably get covered are is as follows:

    Healthcare : In healthcare and other industries, predictors are most useful when their knowledge can be transferred into action. The willingness to intervene is the golden key to harnessing the power of historical and real-time data. More importantly, to best judge the efficacy and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred. Predictive analytics can also be used  in healthcare to mediate hospital readmissions.

    Insurance : Predictive analytics use has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling compared to 69% of companies with less than that amount of premium.

    Retail : Optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them an insight of the regular happenings in the retail sector.

    Internet : Internet analytics is the collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. In this class, we explore a number of key functions of such online services that have become ubiquitous over the last couple of years. Specifically, we look at social & information networks, recommender systems, clustering and community detection, dimensionality reduction, stream computing, and online ad auctions.

How do I get certified in Business Analytics & R?

  • Participants get certified in Business Analytics & R by:
    • Completing any one project out of the four given by JK Michaels.  The outcome of the project should be displayed to the lead trainer and the candidate is evaluated thereafter. Necessary screenshots of the project should be mailed to mails@jkmichaelspm.com
    • Clearing the online examination with a minimum score of 80%.
    Note: It is mandatory that a participant fulfills both the criteria i.e. completion of 1 Project and clearing the online exam with minimum score of 80% to be certified.

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