PG Diploma In Data Science

Postgraduate Diploma (PG Diploma) in Data Science is an advanced academic program designed to equip individuals with comprehensive knowledge and skills in the field of data science. It is tailored for students and professionals seeking expertise in data analysis, machine learning, statistics, programming, and other related areas.

Program Overview:

Curriculum and Subjects Covered:

A PG Diploma in Data Science typically encompasses a diverse range of subjects, including:

  1. Statistical Analysis: Understanding and application of statistical methods for data interpretation.

  2. Machine Learning: Techniques and algorithms for pattern recognition and predictive modeling.

  3. Data Mining: Extracting useful patterns and information from large datasets.

  4. Programming Languages: Proficiency in languages like Python, R, SQL, and others used for data manipulation and analysis.

  5. Data Visualization: Creating visual representations to comprehend complex data.

Data Science And Data Analytics​

Data science and data analytics are two important fields in the world of information technology that deal with handling and analyzing data to gain valuable insights.

Data science involves the overall study of data. It includes various processes such as collecting, cleaning, processing, and analyzing large sets of data to extract meaningful patterns, trends, and predictions. Keywords related to data science include: data mining, machine learning, predictive modeling, artificial intelligence, and big data.

On the other hand, data analytics specifically focuses on analyzing data to understand what it means and how it can be used to make informed decisions. It involves using statistical and analytical tools to interpret data and draw conclusions that can help businesses or organizations make better choices. Keywords associated with data analytics are: statistical analysis, business intelligence, data visualization, and descriptive analytics.

Both data science and data analytics play crucial roles in various industries today, including finance, healthcare, marketing, and technology. They help organizations make data-driven decisions, improve processes, enhance efficiency, and gain a competitive edge in their respective markets.

Professionals in these fields often need skills in programming languages like Python, R, or SQL, as well as expertise in using specialized tools and platforms such as Tableau, TensorFlow, or Hadoop.

Benefits of Pursuing a PG Diploma in Data Science From LA bootcam

Pursuing a Postgraduate Diploma in Data Science from LA bootcamps offers several advantages

  1. Industry-Relevant Curriculum: LA bootcamps often design their programs in collaboration with industry experts, ensuring that the curriculum is up-to-date and aligned with current industry needs and trends in data science.

  2. Hands-on Practical Learning: These programs typically emphasize hands-on, practical experience with real-world data sets and projects. This approach helps students gain valuable skills that are directly applicable in professional settings.

  3. Networking Opportunities: LA bootcamps often provide opportunities for students to connect with professionals, mentors, and potential employers through workshops, seminars, and networking events, enhancing their professional network within the data science field.

  4. Career Support Services: Many bootcamps offer career support services, such as resume building, interview preparation, and job placement assistance. This can significantly aid students in securing internships or full-time positions after completing the program.

  5. Flexibility and Intensive Learning: PG Diploma programs in data science from bootcamps are often designed to be intensive and condensed, allowing students to gain comprehensive knowledge and skills in a shorter time frame. This flexibility might suit individuals looking for a quicker entry into the field.

  6. Diverse Learning Environment: LA bootcamps attract students from diverse educational and professional backgrounds, fostering a rich learning environment where students can learn from each other’s experiences and perspectives.

  7. Specialized Focus Areas: Some bootcamps offer specialized tracks or modules within data science, allowing students to focus on specific areas like machine learning, big data analytics, or data visualization based on their interests and career goals.

  8. Credential and Recognition: Completing a PG Diploma in Data Science from a reputable LA bootcamp can add credibility to your skills and knowledge in the eyes of employers, potentially improving your job prospects in the competitive data science job market.

FAQs) PG Diploma in Data Science from LA Bootcamp:
  1. What is the duration of the PG Diploma in Data Science program at LA Bootcamp?

    The program typically spans over a period of 12 months. However, the duration may vary depending on the specific curriculum and any additional elective courses or projects undertaken by the student.

  2. What are the prerequisites for enrolling in the PG Diploma in Data Science program?

    While specific prerequisites might differ, generally, applicants are expected to have a bachelor’s degree in a related field like computer science, mathematics, statistics, or engineering. Proficiency in programming languages such as Python, knowledge of statistics, and a basic understanding of data analysis concepts are also beneficial.

  3. What topics or subjects are covered in the curriculum of this program?

    The curriculum usually covers a broad spectrum of subjects including but not limited to machine learning, data analysis, data visualization, big data technologies, statistical modeling, predictive analytics, and deep learning. Some programs might also include modules on business intelligence and data-driven decision-making.

  4. Does the program offer any hands-on projects or practical experience?

    Yes, most PG Diploma programs in Data Science at LA Bootcamp incorporate hands-on projects, case studies, and real-world applications to provide students with practical experience. These projects aim to simulate industry scenarios and enable students to apply their knowledge in solving authentic data-related problems.

  5. What career opportunities can graduates expect after completing this program?

    Graduates can pursue various career paths in the rapidly growing field of data science. They might find opportunities as data analysts, data scientists, machine learning engineers, business analysts, or data engineers in diverse industries like finance, healthcare, e-commerce, marketing, and more. LA Bootcamp often assists students with career support services such as job placement assistance and networking opportunities to help them enter the industry.

Tim Berners-Lee

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 / 4 days a week

Weekends – 4 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
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.



  • 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

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 Engineer

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.

Data Analyst

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 Scientist

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.

Marketing Analyst

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

Never settle for less

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