About Data Scientist Course in Bangalore
Looking for Best Data Science Training in Bangalore? your search ends here. Being one of the coveted institute offering Data Science training, Datamites has already groomed and is still grooming many professionals into this most demanding Data Science field. Data Science uses systems, procedures, and processes to extract useful information from unstructured or structured Business data. Many students and professionals are opting this course to excel in their career.
Our Data Scientist course is designed to cover the beginner's level to advanced level. The practical and real time projects that we offer, make our candidates ready for a challenging work environment. Data science is seeing an increased demand, only with handful professionals already existing in this field, they couldn't seem to quench this thirst. It is the right time to choose this course in order to groom your Data Scientist career to achieve great benefits.
Why this course "Certified Data scientist with R"?
This course comes as a perfect package by allowing you to learn about entire data lifecycle and all the methods to achieve the same. You would not just learn about the data analytics techniques and tools but also learn on how to apply them to raw data and obtain business insights.
Advantages of learning course "Certified Data scientist with R"
- Data science is the hot field that is booming up now. Be it a small company or an MNC, they need a Data scientist to manage their large pool of data.
- High demand for data scientists with only a few qualified people to hire, the companies are ready to offer extremely fat salaries to those who have completed this course.
- This course not only fetches you loads of career opportunities but also allows you to apply the new age skills in your current work and prove to senior management that you are efficient and knowledgeable.li>
- Be assured that you are entering the future of analytics much earlier to grab those wonderful opportunities arising from this biggest need of the business world.
Who should choose Data Scientist Course?
This course "Certified Data scientist with R" is not restricted to any specific domain.
- Fresh Graduates or students from any discipline can choose this course to obtain better job opportunities in this most demanding data analytics field.
- Working professionals looking to change their domain to analytics field can also opt this course.
- Highly recommended for those who are aspiring jobs that mainly revolves around data analytics and for those who have a keen interest or a basic knowledge of statistics and mathematics.
Why do you need to choose DataMites to do this course?
Being one among the handful companies offering this Data Science training, DataMites assures you an industry tailored training program from best trainers. The combined learning of theoretical and practical sessions can help you with a clear idea of all the concepts. DataMites's Certified Data scientist with R course will give you the edge in the competitive market.
FAQ's
- Is this an online or distance course?
This an online program and you can learn conveniently from your home.
- What are the unique features of this course?
This course is perfectly aligned to the current industry requirements and gives exposure to all latest techniques and tools. The course curriculum is designed by specialists in this field and also have been monitored by industry practitioners.
- Do I need to pay separately for the examination?
No, the exam fees are already included in the course fee and you will not be charged extra.
- Do I get job assistance after the course?
We guarantee that you can achieve an in-depth knowledge and an enhanced skills but we do not provide job assistance.
Data Science Course Curriculum:
This course covers following concepts.
1) Data Visualisation with Tableau?
The following topics are covered here
Visual Analytics with Tableau:
- Introduction to Visual analytics
- Principles of Visual analytics
- Data Interpretation
- Pivot Tables
- Split Tables
- Time Series Chart
Various Graphical Representations:
- Bar Plot
- Scatter Plot
- Istograms
- Box plot
- Word Cloud
- Bubble Chart
- Side by Side chart
- Dual Combinated Chart
Creating Fields:
- Quick table calculation
- Ad-Hoc
- Calculated fields
- Lookup
- If-Else
- Case
- ZN
- Parameter
Working on Maps:
- Symbol Maps
- Animated Maps
- Filled Maps
- Actions between Maps
- Sets
- Dashboards and Storytelling
2) Statistical Analytics using MiniTab
The following topics are covered here
Business Statistics:
- Various Data Types a)discrete, b)continuous, c)Nominal, d) Ordinal, e) Interval Scale, f) Ratio
- Central Tendency
- Measures of Dispersion
- Random Variable
Analysis:
- Probability Distribution
- Normal Distribution
- Skewness
- Kurtosis
- Random Sample
Confidence Interval:
- Confidence Interval
- Sampling Frame
- Z-Calculations
- Central Limit Theorem
- Chi-Square Test
Hypothesis:
- Hypothesis Testing
- Converting Problem to Statistical Problem
- Null Hypothesis
- Alternate Hypothesis
- Case Study on Hypothesis Testing
3) Data Mining using R Language
The following topics are covered here
Introduction to R:
- Installing R
- Installing R Studio
- Creating Objects in R
- Creating Arrays
- Creating Data frames
- Use of Structure
- Dimensions
- Loading CSv files, Foreign packages into R
Data Manipulation with R:
- Loading vectors in R
- Combining to vectors in R
- Cleaning Data with R, Swapping Data, Sorting Data, Converting unstructured to structured data, usage of sub, gsub, regexpr, gregexpr, apply, lapply, sapply
Data Visualization with R:
- Usage of Plot, lines, boxplot, stars, barplot, pie, hist, rug, sunflowerplot, various color of histograms, tabplot, ggplot2, maptools and extracting data from URLs
4) Machine Learning
The following topics are covered here
Foundation
Machine Learning Introduction: Supervised and Unsupervised Learning
- Linear Regression Theory
- Linear Regression Programming with R
- Working on Case Study
Multiple Linear Regression
- Theory behind multiple linear regression
- Multiple Linear Regression with R
- Working on Case Study
Decision Tree
- Theory Behind Decision Tree
- Decision Tree with R
- Working on Case Study
Naive Bayes
- Theory behind Naive Bayes classifiers
- Naive Bayes Classifiers with R
- Working on Case Study
Support Vector Machines
- Theory behind Support Vector Machines
- Support vector machines with R
- Improving the performance with Kernals
- Working on Case Study
Association Rule
- Theory behind Association Rule
- Working on Case Studies
Expert
Neural Net
- Artificial Neural Network
- Connection Weights in Neural Network
- Generating Neural Network with R
- Improving Neural Network Accuracy with Hidden Layers
- Working on Case
Random Forest
- Theory behind Random Forest
- Random Forest with R
- Improving performance of Random Forest
- Working on Case Study
Recommendation Engine
- Theory behind Recommendation Engines
- Working on Case Study with R
Dimension Reduction
- Theory behind Recommendation Engine
- Working on Case Studies
5) Text Mining
Text Mining:
- Introduction to Text Mining concepts
- Sentiment Analysis with R
- Positive and Negative Word Cloud
- Case study on Sentiment analysis
Advanced Regression
- Theory Behind Advanced Regression
- Advanced Regression with R
- Working on Case Study
6) Web Analytics
- Theory behind Web Analytics
- Working on Case Study