Data Analyst Training Program

Transcription

1 R Data Analyst Training Program In exclusive association with 21,347+ Participants 10,000+ Brands Trainings 45+ Countries [Since 2009] Training partner for

2 Course Highlights Who is this Course for Salient Features Programmers and Statisticians 3 Hrs/Week Live Instructor-Led Online Sessions 15 Days of Project Work Active Q/A Forum Class Labs/Home Assignment (10 hours/week Learning Time) Govt. of India (Vskills Certified Course) Placement Support Personalised Training Program Lifetime Access to Updated Content and Videos Industry and Academia Faculty Course Advisors Top Data Analytics Tools Covered Specialize in R Industry s Data Analytics Advisors Ajay Ohri Data Scientist Ajay Ohri is a Data Scientist and Blogger in an open source data science. Since 2007, he has published his blog DecisionStats.com. Manas Garg heads the Analytics for Marketing at Paypal. He takes Data Driven Decisions for Marketing Success. Manas Garg Architect Shweta Gupta Vice President, Tech. Shweta Gupta has 19+ years of Technology Leadership experience. She holds a patent and number of publications in ACM, IEEE and IBM journals like Redbook and developerworks. Vishal is a Technology Influencer and CEO of Right Relevance. (A platform used by millions for content & influencer discovery) Vishal Mishra CEO & Co-Founder

3 Course Instructors NITIKA MALHOTRA Nitika Malhotra is a Data Scientist at Zomato and handles data science and machine learning projects. She has worked as the Analytics Specialist at Transorg, Research Associate at IIT-Delhi and Research Intern at MOSPI (Ministry of Planning and Programme Implementation). She holds expertise in Probability, Statistics, Data Structures, PostgreSQL, R, SPSS, Pentaho, SAS, Machine Learning, and Hive. SHANTANU GARG Shantanu Garg is the Sr. Marketing Analyst at MakeMyTrip. He handles data science and web analytics projects. He has worked as the Analytics Specialist in Transorg and Research Associate for Nielsen. He is skilled in Probability, Statistics, Data Mining, PostgreSQL, R, Pentaho, Machine Learning, Adobe Analytics, Hive and Google Analytics. Course Curriculum The R for Data Analytics course is thoughtfully designed to allow learners with some programming background to make a transition into the analytics industry with correct skillsets using R language. It is designed in a way that the student starts with the introduction to R programming, and in a very hands-on learning method using R Studio, will learn the nuts and bolts of R to perform the role of data analyst. The student will progress to applied statistics and machine learning concepts & applications. Post completion of the program, learners will be prepared to device solutions for real-time problems in the industry. INTRODUCTION TO DATA ANALYTICS This will be an introduction session with a brief explaination about Data Analytics ecosystem, scope of this field and introducton to R platform. Introductory Session Briefing about Analytics domain How insides from data can help business solve day-to-day problems and find solution Various platforms which can help you in the journey of becoming Data Scientist Introduction to R as a platform

4 INTRODUCTION TO R PROGRAMMING This session will be an introduction to Basics of coding on R Studio platform. R Nuts and Bolts Understanding different windows of R Studio Basics of R Programming and some important rules for coding in R Installing predefined packages Entering inputs and R objects (Vector, Matrix, Dataframes and Factors) R Datatypes Using dplyr Package Text Manipulations using Strings Reading data (csv file) in R DATA MANIPULATIONS AND LOOPING IN R In-depth understanding about data manipulation using different packages and functions & conditional loopings in R. In Detail Hands on for Learning Data Manipulations Subsetting dataset Date and Time in R Loops: while & for Conditionals: if-else Functions: Defining functions, Anonymous functions Apply family of functions Sampling in R EXPLORATORY ANALYSIS IN R Exploratory Analysis will help you know more about the features of datasets, statistically. For understanding real-time data in the industry, this is the first step. Descriptive Statistical Analysis Central Tendencies Measurements of Dispersion Test of Normality Null Value Treatment Outlier Treatment Correlation Analysis Reshaping Data Merging Data

5 VISUALISATION Creating basic as well as interactive visualisation in R. R Studio Visualisations Interactive Dashboard Categorical Data: Barplot,Pie Chart Numeric: Boxplot, Histogram, Scatter Plot, Line Chart Using different libraries to make graph presentable (ggplot2, Rcolorbrewer) Using shiny to create interactive Graphical Dashboards INFERENTIAL ANALYSIS IN R Inferential Analysis is very useful in knowing underline information of data. It is generally used in the industry for A/B or Test/Control group comparisons. Parametric Statistical Tests Non-Parametric Statistical Test Basic theory of Inferential Statistics Hypothesis tests using Z Test T-statistics Test Two sampled Z Test and T Test ANOVA Post-hoc Test Wilcoxen Test Mann-Whitney U Test K.S. Test Runn Test Chi-Square Test DATA LOADING AND FILE FORMATS This section begins with loading and bringing data from different data sources in R. Descriptive Statistical Analysis Data loading and file formats Loading JSON files XML and HTML Web Scraping Interacting with HTML and Web APIs Interacting with databases Text Mining/Text Analytics in R

6 MACHINE LEARNING Introduction to machine learining and its further bifurcations. Learning most of the industry-wise used machine learning techniques. What is Machine Learning Machine Learning real-world Examples Assumptions for Linear Regression Supervised Learning Techniques Linear Regression Assumptions checks in R Building Linear Regression Model in R Stepwise method Case Study- Linear Regresssion Exploring Data Dividing data into Test and Train Model Building and R Predicting on Test Data using Model Logistic Regression Understanding Logistic Regression Classification Model Building using Logistic Model Confusion Matrix Random Forest Decision Tree Random Forest SVM and Naive Bayes SVM Naïve Bayes Unsupervised Learning Techniques Unsupervised Learning Clustering K-means Hierarchical Clustering Time Series Analysis

7 Capstone Project (3 Weeks) The Capstone project is the culminating assignment that will allow you to have an integrated experience of the program. The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data analytics process. The real world is filled with text data and is usually messy hence cleaning and handling text is an important step towards making smarter Machine Learning algorithms. You will be working on one such usual messy dataset which hides a lot of information under the hood which is awaiting to be discovered. Tools Duration Fee Batch Options 18 Weeks Rs. 34,900+GST Weekend Interested? Contact Us! info@digitalvidya.com Attend a Free Orientation Session: