This course will help to develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics. At the end of the course you would be able to:
- Apply quantitative modeling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.
- Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
- Apply principles of Data Science to the analysis of business problems.
- Use data mining software to solve real-world problems.
- Employ cutting edge tools and technologies to analyze Big Data.
- Apply algorithms to build machine intelligence.
What is Data Science ?
Data Science as a multi-disciplinary subject encompasses the use of mathematics, statistics, and computer science to study and evaluate data. The key objective of Data Science is to extract valuable information for use in strategic decision making, product development, trend analysis, and forecasting.
Data Science concepts and processes are mostly derived from data engineering, statistics, programming, social engineering, data warehousing, machine learning, and natural language processing. The key techniques in use are data mining, big data analysis, data extraction, and data retrieval.
Data Science deals with identification, representation, and extraction of meaningful information from data sources to be used for business purposes. Data engineers are responsible for setting up the database and storage to facilitate the process of data mining, data munging and other processes.
Data Science Benefits:
- Better business value
- Identification and refining of target audiences:
- Better risk analysis:
- Recruit better in lesser time:
The Data scientist gathers datasets from multi-disciplines and compiles it. Then he applies machine learning, predictive and sentimental analysis to analyze the data and eventually deciphers a meaningful pattern out of the huge datasets. A data scientist makes use of tools and languages like R, MATLAB, and DB Management for data analysis and machine learning.
Big Data, on the other hand, is an advanced science that aims at finding solutions to process, store and extract relevant information in a huge quantity of data, which can be relational (as in Excel Sheets or databases) or non-relational (such as tweets, images, or video).
Example of Big Data Tools: Apache Hadoop (free) and Bluemix of IBM.
Big Data management and processing strategies are part of Data Science. Data is collected to perform some analysis on it, therefore, data analysis is part of Data Science. Since all analysis problem or patterns cannot be identified with statistical analysis, machine learning is often used to identify patterns from data.
Data Analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is also known as data analysis.
Data analysis involves examining data to find useful information for achieving organizational goals. Analytics sorts data into things that organizations may or may not be aware of and can be used to measure events in the past, present, or future. Data Analytics often moves data from insights to impact by connecting trends and patterns with the company’s true goals and tends to be slightly more business and strategy focused.
The Data Scientist and Data Analyst are different. The Data Scientist starts by asking the right questions, while Data Analyst starts by mining the data. The Data Scientist needs substantive expertise and non-technical skills whereas a Data Analyst should have soft skills like intellectual curiosity or analytical skills.
Inventor of the World Wide Web
“Data is a Precious thing and will last longer than the systems themselves”
Duration: 12 MONTHS
Weekdays – 2 Hrs / 3 days a week
Weekends – 3 Hrs / Sat & Sun
Unit 1: Introduction to Data Science
Unit 2: Statistics for Data Science
Unit 3: The Basics of Python and R
Unit 4: Data Preparation
Unit 5: Exploratory Data Analysis
Unit 6: Preparing to Model the Data
Unit 7: Decision Trees
Unit 8: Model Evaluation
Unit 9: NAIVE BAYES Classification
Unit 10: Neural Networks
Unit 11: Clustering
Unit 12: Regression Modeling
Unit 13: Dimension Reduction
Unit 14: Generalized Linear Models
Unit 15: Association Rules
Unit 16: Data Summarization & Visualization
- LIVE ONLINE SESSIONS CONDUCTED BY TRAINER AS PER FIXED ACADEMIC CALENDAR
- PRACTICAL SESSIONS IN LABS OR VIA REMOTE ACCESS
- RECORDED VIDEO SESSIONS AVAILABLE
- ASSIGNMENTS & EXERCISES
Students who have successfully completed their course as per Terms and conditions will be eligible for the certification.
Certification in Cyber Security will be provided jointly by St. Xavier’s Technical Institute and L.A Bootcamps.
St. Xavier’s Technical Institute is affiliated to Directorate of Technical Education and approved by All India Council for Technical Education (AICTE) and autonomous institute.
- 12th Pass / Appearing Students
- Diploma students (any stream) Studying or Passed out students
- BE/BTech (Any stream) Studying or Passed out students
- Graduate (any stream ) Studying or Passed out students
- Working professionals in the IT & Tech sector
Data Science as a multi-disciplinary field revolves around reading and processing data, pulling knowledge from that data. Having a qualified resource pool is essential for useful data conversion.
Data Science is for people who have a penchant for analyzing and explaining information in an intriguing manner. Data Science career is ideally suited for analytical and mathematical minds to analyze data. Having technical skills (in computer science, end-to-end development, and coding) is also a necessary requirement.
An aspiring Data Scientist should also a good communicator so that he can present complicated data insights in an interesting manner. Other soft skills like team spirit, business acumen, and the quest for knowledge are important.
Most Data Scientists work as researchers, but there are other roles available as developers or in business management. You may start working as a business analyst or a programmer and later switch to DS, with some years of experience.
Data scientists are no longer restricted to only information technology industries. From retail to finance, supply chain to entertainment and transport to government, data scientists use data and continue to tackle real-world problems.
Among the top 20 skills in demand in today’s workforce, artificial intelligence (AI) and machine learning are in high demand, ranking at #2. There is a growing need for data scientists and analysts globally to help navigate a disruptive marketplace, governed by big data. The C-Suite at enterprises turns to data scientists to connect the dots across terabytes of data to offer trends, predictions, and insights to drive competitive advantage.
Data Architect and Administrators
Visualizers of the data management framework for the entire organization, data architects work closely with data engineers. They primarily work on understanding enterprise strategy and data that needs to be collected. They then create new database systems or enhance the performance of existing systems. Additionally, data architects design the flows and processes for data management and data engineers build the infrastructure.
Data engineers are experts at accessing, and moreover, processing vast amounts of real-time data. Vital to technology-driven companies and tech departments, they interpret unformatted and unverified data. Data engineers set up the infrastructure using programming languages (Python) and advanced SQL, NoSQL.
Most data scientists start as data analysts and data engineers at the beginning of their careers. Data analysts work directly with raw data collected through the systems. This also means they work with various teams like marketing, sales, customer support, finance to process information. Data analysts clean the data, study, and create reports using data visualization tools like Tableau and Excel to help teams develop strategies.
Data Scientists go beyond analyzing big data to address real-world business problems. The company leadership relies on data scientists to provide trends, patterns across data and offer actionable insights and strategies that can affect the bottom line. Their insights have a direct impact on strategic business decisions.
Machine Learning Engineer
A Machine Learning Engineer is a unique combination of software engineering and data science that works with big data daily. In a large consumer-facing setup both roles work together but may have independent responsibilities. Data scientists are expected to be machine learning experts with advanced software programming skills. ML Engineers develop software, ML models, and artificial intelligence (AI) systems to drive various processes for the organization.
Statisticians and Mathematicians Prominently working in the government, healthcare, and research and development organizations, statisticians identify trends that advise decision-making and policies in organizations. Mathematicians and statisticians interpret large volumes of numerical data and design research surveys, develop mathematical models to collect data as well as report findings.
Business IT Analyst
Strategists at heart and analysts by mind, a business analyst evaluates a company’s processes and analyses industry trends and markets. Business analysts process enormous amounts of data and scout opportunities to improve business revenue and growth. Common job titles held are business intelligence (BI) developers and business consultants. Processing this data requires a BI developer to have advanced skills in BI analytic tools and programming skills.
Identifying shifting consumer behaviors and examining new buying trends as well as analyzing the digital universe for a business is all the excellence of a market analyst. With most businesses selling digitally, marketing analysts access large amounts of data across various platforms and devices to create strong go-to-market strategies and evaluate marketing campaigns.
Clinical Data Managers
Clinical data managers unite healthcare training with mathematics, programming, computer science, and statistics. Similar to the other fields, data collection to data governance and data integrity across clinical trials and research, clinical data managers actively assimilate, analyze and predict medical industry trends.
L.A Bootcamps will provide 100% Placement Assistance to all students who successfully complete the course and extend below complimentary placement services to the students:
- Resume Building assistance
- Career Mentoring with Industry Professionals
- Interview Preparation sessions
- Career Fairs
- Internship opportunities for Freshers