Data Science Training In Bangalore
8th JulyDuration:35 Hrs.Class Days:Weekend - SatClass Time (IST):2:00 PM ISTDurationClass DaysClass Time (IST)Enrollment
10th JulyDuration:35 Hrs.Class Days:weekday - MonClass Time (IST):10:00 AM IST
15th JulyDuration:35 Hrs.Class Days:weekend - SatClass Time (IST):1:00 PM IST
17th JulyDuration:35 Hrs.Class Days:weekday - MonClass Time (IST):9:00 AM IST
Who is data scientist?
Data Scientist is a person who analyses entire accumulated data from various sources, across various machines concerned with the subject or a product in order to provide suitable insights to his company. The growth of the company depends upon the strategical plans drawn by forecasting marketing changes depending upon the insights provided by their data science. The study of the accumulated data across from all the sources, consumer network, competitors’ information, sales records, brand image inputs, consumer reviews, political conditions, financial conditions, everything related to the product defines specific status of the product in the market, thus enable the data scientist to provide suitable analysis report to his company. Information explosion in these days due to the invention of the internet made it extremely necessary for the business organizations to equip themselves with an expert and efficient data analyst in their organization to thrive ahead.
Why it is necessary to learn Data Science?
It is necessary to learn the data science is more than ever in the present business scenario. The need to fill the void for an able data scientist is necessary ever than before. Gone are the days of brick and mortar business. Present business need to have powerful tools to meet the global competition. Unknown challenges, unexpected changes in political and economic status in the global market need to be met suitably by effective business strategies. It is necessary to learn the data science for the following reasons explained below for the benefit of those who wish to take up this course.
Connect with consumers effectively, knowing them directly help to develop higher end products that are beneficial both the ways.
- De-redesign the products to meet consumer expectations
- Allows performing risk analysis
- Helps to keep your data safe and secure
- Explore new revenues based on the data analysis
- Customize all the transactions by making real time analysis
- By applying suitable strategies, it will allow organizations to reduce maintenance cost
- Allows business organizations to get wider prospective of the concerned business
All the above mentioned features are extremely essential for the growth of any business environment; therefore it is easy to understand the importance of data science and scientist in an organization.
Who is best fit to take up this course?
- IT professionals
- Business Analysts
- Data Analysts
- Ware house managers
- Application Developers
- Job seekers in IT industry
- Business operators
What are the course objectives of Data Science?
- Learners are taught to understand business intelligence and business analytics
- To understand the business data analysis through the powerful tools of data application
- Learn how to apply Tableau, MapReduce, and get introduced in to R and R+
- Understand the methods of data mining and creation of decision tree
- Explore different aspects of Big Data Technologies
- Learn the concepts of loop functions and debugging tools
What are the pre-requisites for the course?
Learners should have an aptitude for analytical thinking. It is necessary to have quantitative skills. Technical background and programming knowledge will be an added advantage to join this course. Anybody who is interested in the field of data analytics can join the course to hone their skills.
Data Science Course
Learning Objectives – Data scientist certification in India module one starts with the basics of Business Analytics, R basic, R programming, R’s role in solving analytical problems, R’s popularity in tech giants like- Facebook, Google, Finance etc.
Topics – Business Analytics, R, R language and programming, Ecosystem, Uses of R, Data types in R, subsetting methods, R comparison with other software’s, R installation, operations in R, useful packages, IDER, GUI, using functions like- length(), str(), ncol() etc, summarize data.
Data Manipulation and Data Import Techniques in R
Learning Objectives – In this module, we will discuss about dirty data set, data cleaning, which results in data set, and ready for analysis, exploring functions, versatility of R, robustness of R. This module helps you to understand various importing techniques in R
Topics – Data Cleaning, Data Inspection, Troubleshooting the problems, Function uses, grepl(), sub(), grep(), Data coerce, apply() functions uses, Import data from spreadsheets into R, Importing text files into R, import data, installation of packages. RDBMS from R, using ODBC, SQL queries, Web Scraping.
Exploratory Data Analysis
Learning Objectives – Data scientist certification in Bangalore module 3 includes information about exploratory data analysis, EDA- for observing what data tell us beyond the formal hypothesis. Typical EDA process.
Topics – Exploratory Data Analysis, Implementing EDA on datasets, Box plots, cor() in R, EDA functions, list(), summarize(), Multiple packages in R, various plots, Segment plot, HC plot in R.
Data Visualization in R
Leaning Objectives – In this module you will learn about- visualization in USP of R, creating simple visualization in R, complex visualization in R.
Topics – Data Visualization, Graphical representations, Functions in R, Plotting graphs like box plot, table plot and histogram, Improvising plots, GUIs, Deducer, R Commander and Spatial Analysis.
Data Mining: Clustering Techniques, Decision Trees and Random Forest
Learning Objectives – In this module we will discuss about various Machine Learning algorithms, Machine Learning types- Unsupervised Learning and Supervised Learning, Difference between Unsupervised Learning and Supervised Learning. We will also learn about K-means Clustering and implementing them, Concepts of Decision trees and Random forest, Algorithm creation of forests and trees, LMS.
Topics – Data Mining, Machine Learning algorithms, Unsupervised and Supervised Machine, K-means clustering, Decision Trees, Random forest Algorithm creation, Entropy, features and working of Random forest.
Linear and Logistic Regression
Learning Objectives – Data science training in Bangalore, module 6, we will introduce- Regression Techniques, Linear and logistic regression basics, implementation in R.
Topics – Simple and Multiple Linear Regressions, Simple and Multiple Logistic Regression, Log Linear Model.
Anova and Predictive Analysis
Learning Objectives – In this module we will discuss about the Analysis of Variance, Anova Technique, Predictive Analysis.
Topics – Anova Technique, Sample testing, One and Two way Anova, Predictive Analysis.
You will be given a problem statement and in this module our qualified professionals will help you on that statement.
Perform Exploratory Data Analysis
Establish hypothesis of data.
• Handle outliers
• Troubleshooting missing data.
• Validation datasets.
• Logistic Regression
• Confusion Matrix
• Final model