Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You'll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You'll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR. What are the objectives of this course? After the completion of the course, you should be able to: Gain in ...
Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You'll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You'll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR.
What are the objectives of this course?
After the completion of the course, you should be able to:
Who is it intended for?
The course is designed for all those who want to learn about the life cycle of Data Science, which would include acquisition of data from various sources, data wrangling and data visualization. Applying Machine Learning techniques in R language, and wish to apply these techniques on different types of Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Machine Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. 'R' professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
There is no specific pre-requisite for the course, however basic understanding of R can be beneficial. Teachingkrow offers you a complimentary self-paced course, i.e. "R Essentials" when you enroll in Data Science Certification Training.
What marks this course apart?
The incorporation of technology in our everyday lives has been made possible by the availability of data in enormous amounts. Data is drawn from different sectors and platforms including cell phones, social media, e-commerce sites, various surveys, internet searches, etc. However, the interpretation of vast amounts of unstructured data for effective decision making may prove too complex and time consuming for companies, hence, the emergence of Data Science.Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming. Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract useable and relevant data from the set. Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past. Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime. Machine learning perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at the predicted time.
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