What is Data Science & Data Analytics?
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.
Tim Berners-Lee
Inventor of the World Wide Web
“Data is a Precious thing and will last longer than the systems themselves”
 | Duration: 4 MONTHS Schedule: Weekdays – 2 Hrs / 4 days a week Weekends – 4 Hrs / Sat & Sun |
Unit 1: Getting started with PYTHON
Unit 2: Numerical Computing with Pandas
Unit 3: Scientific Computing with NumPy/SciPy
Unit 4: Presenting stories via simple visualizations
Unit 5: Using the NLTK (Natural Language Toolkit) Package
Unit 6: Getting insights from TWEETS
DELIVERY METHODOLOGY
- SESSIONS CONDUCTED BY TRAINER AS PER FIXED ACADEMIC CALENDAR
- PRACTICAL HANDS ON SESSIONS
- PRACTICAL SESSION ON LABS AND HANDS ON WITH EXPERTS
- ASSIGNMENTS & ASSESMENTS
- PROJECTS AND CASE STUDIES
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.
ADMISSION CRITERIA
- 12th HSC or Passed and Dropouts
- BE/BTech/BSC/BCA/MTech/MSC/MCA/ or Passed out students pursuing or Graduate
- Graduate (any stream ) Studying or Passed out students
- Working professionals in the IT & Tech sector
- Any one is eligible who is excel in this program
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.
Business Intelligence Analyst
A business intelligence analyst’s most fundamental job is to find patterns — and value — in their company and industry data. BI Analysts will be expected to be comfortable analyzing data, working with SQL, and doing data visualization and modeling.
Data Analyst
Data Analysts do exactly what the job title implies — analyze company and industry data to find value and opportunities. Unlike data scientists, they’re typically not expected to be proficient in machine learning. But most data analyst jobs require programming and SQL skills, as well as statistical knowledge, comfort with the data analysis workflow, and data visualization skills.
Marketing Analyst
Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skill set.
Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.
A lot of money could be wasted on campaigns that do not drive traffic, so marketing professionals will continue to need analysts to make smart decisions about how to leverage existing resources.
Data Analytics is also important for careers like Data Scientist, Data Engineer, Quantitative Analyst, Operations Analyst, IT Systems Analyst.
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
