Gyansetu certified course on Big Data Science & Advanced Analytics is intended to start from basics and move gradually towards advancement, to eventually gain working command on Data management & analytics. We understand learning so many technologies can be difficult and hence we at Gyansetu have divided it into easily understandable format that covers all possible aspects of big data.
We all know that Analytics is the future so let’s get a little deeper and explore their differences between Data Sciences, Data analytics and Machine Learning.
Data science is a concept used to tackle and monitor huge amounts of data or big data. Data science includes process like data cleansing, preparation, and analysis. A data scientist would collect data from multiple sources like surveys, physical data plotting. He would then make the data pass through the vigorous algorithms to extract the critical information from the data and make a data set. This dataset could be further be fed to analyzing algorithms, to make more meaning out of it.
What skills are required to make Data scientist?
- Deep knowledge of Python, Spark, Scala, Big Data.
- Knowledge of databases like SQL.
- Good knowledge in the field of Mathematics and statistics.
- Understanding of analytical functions.
- Knowledge and experience in machine learning.
Now, you might be wondering ” What is data analytics then?”
Data analytics is often used by the companies to search for trends in their growth. It often moves data insights to impact by connecting the dots between trends and pattern while Data science is more about just insights. You could say that this field is more focused on businesses and organizations and their growth. You would need skills like, Python, Rlab, Statistics, Economics, and Mathematics to become a Data analyst. Data analytics further bifurcates into branches like Data mining, which involves sorting through datasets and identify relationships.
Predictive analytics: This generally includes predicting customer behavior and product impact.
- Helps during the market research
- Makes the data collected from surveys more usable and accurate in predictions
- This finds application in a number of places
- From weather report generation to predicting a students behavior in schools to predict the outbreak of disease.
Remember how you learned to ride a bicycle? A machine could learn that with the help of algorithms and datasets. Datasets of values basically.
Machine Learning, basically comprises of set of algorithms that could make software and program learn from it’s past experiences and thus make it ore accurate in predicting outcomes. This doesn’t need to be explicitly programmed, as the algorithm improves and adapts itself overtime.
- Skills that you’d need for Machine learning
- Expertise in coding fundamentals
- programming concepts
- Probability and stats
- Data modeling
- Gyansetu trainers are well known in Industry, they are highly qualified working professionals in MNCs, having a wide experience in training industry.
- We provide interaction with faculty before the course starts.
- Our Train the Trainer approach ensures you learn proactively and come out as an expert.
- We are open seven days a week and provide 24×7 Lab Support Services.
Who should go for Advanced Big Data Science & Analytics Course?
Big Data market growing rapidly & data size is increasing day by day and IT needs expert Big Data Professionals in coming years . It will be helpful for persons working in IT as:
1. Testing professionals
2. Senior IT Professionals
3. BI /ETL/DW professionals
4. Developers and Architects
5. Mainframe professionals
Pre-requisites for Advanced Big Data Science & Analytics Training Course?
There will be no pre-requisites but Knowledge of Java, Python & SQL will be beneficial, but not mandatory. Gyansetu provides a crash course for pre-requisites required to initiate Big Data training.
Big Data Case-Studies
After completion of course , you will be able to analyze Large Datasets & will work on a live project using PIG, HBase, HIVE & MapReduce to perform Analysis.
We will work on case studies related to domains like Finance, Media, Media, Stocks & more.
Project #1: Analyze Stock Market Data
Data : Data set contains stock information such as daily quotes ,Stock highest price, Stock opening price on New York Stock Exchange.
Problem Statement: Calculate Co-variance for stock data to solve storage & processing problems related to huge volume of data.
a)Positive Covariance, If investment instruments or stocks tend to be up or down during the same time periods, they have positive covariance.
b)Negative Co-variance, If return move inversely,If investment tends to be up while other is down, this shows Negative Co-variance.
Project #2: Hive, Pig & MapReduce with New York City Uber Trips
Project #3: Airport Flight Data Analysis
1. List of Delayed flights.
2. Find flights with zero stop.
3. List of Active Airlines all countries.
4. Source & Destination details of flights.
5. Reason why flight get delayed.
6. Time in different formats.
Data Analytics with Python Case-Studies
Industry : Politics
Various Steps for Sentimental Analysis:
- Creating the text data from various UCI repository .
- Create the corpus and convert into structural data.
- Tokenize the structural data using various NLP packages.
- Analyze the sentiment of token by polarity checking.
- Compare the negative and positive polarity of text and graph generation on polarity checks.
Industry : Social
- Setup a Connection as Twitter Application developer. (Using Authentication and Registration)
- Extracting twitter data by streaming process.
- Cleaning and steaming the text data using various packages.
- Generate the Word Graph and the frequency count of the words.
- Get the locations of various tweets and predicting the places that are more prominent for the terrorist activities.
Industry : Entertainment
- Import the text data from the various sites.
- Collect the structural data for the each IPL team.
- Analysis the text data and structural data.
- Use Statistical Inference (Linear Regression Model) system on the formatted data.
- Make a confusion matrix for the each team.
- Preparation of probability graph of each team.