From Data to Decision: Excel in Data Science and Big Data Technologies



Data science and big data are interdisciplinary fields, and individuals from various backgrounds can learn and excel in these domains. While a background in computer science, mathematics, or statistics can provide a solid foundation, it’s important to note that individuals from other domains can also transition into data science and big data with the right training and skills. Here are some backgrounds and domains from which people can learn data science and big data:

  1. Computer Science and Engineering: Individuals with a background in computer science, software engineering, or related fields often have the foundational programming skills and knowledge of algorithms and data structures that are valuable in data science and big data.
  2. Mathematics and Statistics: A strong background in mathematics and statistics provides a solid foundation for understanding the mathematical concepts behind machine learning algorithms, statistical modeling, and data analysis techniques.
  3. Physics and Natural Sciences: Individuals with a background in physics or other natural sciences often possess analytical thinking skills and experience working with complex data, making them well-suited for data science and big data roles.
  4. Economics and Finance: Professionals from economics and finance backgrounds often have experience in analyzing large datasets, econometrics, and financial modeling, which are relevant skills in data science and big data.
  5. Business and Marketing: Individuals with a background in business or marketing can leverage their domain knowledge to derive insights from data and make data-driven decisions. They may also have experience in customer segmentation, market analysis, and data-driven marketing strategies.
  6. Healthcare and Life Sciences: Those with a background in healthcare, biology, or life sciences can apply their domain expertise in data science and big data to areas such as clinical research, genomics, personalized medicine, and healthcare analytics.
  7. Social Sciences: Professionals with a background in social sciences, such as sociology, psychology, or political science, can leverage their understanding of human behavior and societal trends to analyze social data and conduct research using data science techniques.
  8. Manufacturing and Engineering: Individuals with a background in manufacturing or engineering can apply data science and big data techniques for quality control, predictive maintenance, supply chain optimization, and process improvement.

In live instructor led online mode with a commitment of 2 hours per day (assuming 5 days classes per week), the estimated duration will be 2 months(80 hours) to cover the curriculum as well as project work. Please note we are counting on your consistency , focus and dedication throughout the learning journey to help you maximizing your  knowledge acquisition and skill development.

Hourly breakdown of the curriculum :-

  • Foundational Concepts: Approximately 10 hours.
  • Machine Learning and Predictive Modeling: Around 22 hours
  • Big Data Technologies and Processing: Approximately 10 hours
  • Advanced Topics in Data Science: Around 10 hours
  • Real-Time Projects: Varies depending on complexity and scope, approximately 28 hours

Remember again , it is crucial to actively engage in the learning process i.e. completing assignments on time , allocating additional hours (beyond  class) for self study and practice , interacting regularly with trainer in class , participating in Q/A sessions can also enhance the learning experience and ensure a comprehensive understanding of the curriculum.

Also note we are very flexible in terms of  duration of the training. If an individual choose to join twice on certain topics for a deeper understanding – we will allow that and allocate him/her in our multiple batches . But in that case total timeline of the course for that student will be extended accordingly.


Job opportunities in data science and big data are expanding rapidly, offering attractive salaries and career growth potential. Here are some of the exciting roles you can pursue after completing our training program:

  1. Data Scientist: Utilize your expertise in statistical analysis, machine learning, and data modeling to extract insights and drive data-driven strategies. Estimated Salary for entry level ₹8-12 lakhs per annum. If you are more experienced then you can secure upto 14 lakhs.
  2. Big Data Engineer: Design and implement large-scale data processing systems, working with technologies like Hadoop, Spark, and NoSQL databases. Estimated Salary: ₹10-15 lakhs per annum.
  3. Machine Learning Engineer: Develop and deploy machine learning models to automate processes, improve predictions, and enhance business operations. Estimated Salary: ₹9-13 lakhs per annum.
  4. Data Analyst: Analyze and interpret data, transforming raw information into meaningful insights that guide decision-making. Estimated Salary: ₹6-10 lakhs per annum.
  5. Business Intelligence Analyst: Create visually compelling dashboards and reports, providing actionable insights to stakeholders for strategic planning and performance evaluation. Estimated Salary: ₹7-11 lakhs per annum.
  6. Data Architect: Design and manage data systems, ensuring scalability, security, and efficient data flow within an organization. Estimated Salary: ₹12-18 lakhs per annum.

These are just a few of the many career paths open to data science and big data professionals. As the demand for these skills continues to soar, the potential for career advancement and high earning potential is tremendous.

Join our Data Science and Big Data Training program today and embark on a transformative journey to become a sought-after expert in this thriving field. Unlock limitless career opportunities and shape the future of data-driven decision-making.



Course Outline
Introduction to Data Science and Big Data
  1. Fundamentals of data science, big data, and their applications
  2. Introduction to data analysis, data mining, and predictive modeling
  3. Overview of big data technologies and distributed computing frameworks
Data Manipulation and Visualization
  1. Data wrangling and cleaning techniques using Python or R
  2. Exploratory data analysis (EDA) and data visualization using libraries like Pandas, NumPy, and Matplotlib
  3. Introduction to SQL for querying and manipulating relational databases
Statistics and Probability for Data Science
  1. Statistical concepts and hypothesis testing
  2. Probability distributions and their applications
  3. Statistical inference and regression analysis
Machine Learning Algorithms
  1. Supervised learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests)
  2. Unsupervised learning algorithms (e.g., clustering, dimensionality reduction)
  3. Model evaluation and performance metrics
Big Data Processing and Technologies
  1. Introduction to Hadoop and MapReduce framework
  2. Apache Spark for distributed data processing and analytics
  3. NoSQL databases (e.g., MongoDB, Cassandra) for handling large-scale data
Deep Learning and Neural Networks
  1. Introduction to deep learning and neural networks
  2. Building and training neural networks using libraries like TensorFlow or PyTorch
  3. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
Data Science Project Lifecycle
  1. Understanding the project lifecycle in data science
  2. Data acquisition, cleaning, and preparation
  3. Feature engineering, model building, and evaluation
  4. Model deployment and monitoring
Advanced Topics in Data Science
  1. Natural Language Processing (NLP) for text mining and sentiment analysis
  2. Recommendation systems and collaborative filtering
  3. Time series analysis and forecasting

Data Ethics and Privacy
  1. Ethical considerations in data science and big data projects
  2. Privacy and data protection regulations
  3. Bias and fairness in algorithmic decision-making
Capstone Projects
  1. Customer Segmentation for an E-commerce Company:
    • Objective: To identify distinct customer segments based on purchasing patterns, demographics, and browsing behavior, enabling targeted marketing strategies and personalized customer experiences.
    • Tools and Technologies: Python (NumPy, Pandas, Scikit-learn), SQL, data visualization libraries (Matplotlib, Seaborn)
    • Expected Outcome: Segmented customer groups, visualization of customer profiles, recommendations for personalized marketing campaigns.
  2. Fraud Detection in Financial Transactions:
    • Objective: To develop a real-time fraud detection system that can identify anomalous patterns and detect fraudulent activities in financial transactions, minimizing financial losses and protecting customers.
    • Tools and Technologies: Python (Scikit-learn, TensorFlow), Apache Spark, big data processing frameworks (Hadoop, Hive), anomaly detection algorithms
    • Expected Outcome: Machine learning model for fraud detection, real-time monitoring system, identification and prevention of fraudulent transactions.
  3. Predictive Maintenance for Industrial Equipment:
    • Objective: To build a predictive maintenance solution that analyzes sensor data from industrial machinery, predicts potential failures, and recommends maintenance actions, minimizing downtime and improving operational efficiency.
    • Tools and Technologies: Python (Pandas, Scikit-learn), Apache Spark, sensor data processing, machine learning algorithms (classification, regression)
    • Expected Outcome: Predictive maintenance model, alerts for maintenance activities, reduction in unexpected equipment failures.
  4. Sentiment Analysis for Social Media:
    • Objective: To analyze sentiment trends on social media platforms, understand public perception, monitor brand sentiment, and identify emerging issues or opportunities.
    • Tools and Technologies: Python (NLTK, Scikit-learn), natural language processing (NLP) techniques, sentiment analysis algorithms, social media APIs (e.g., Twitter API)
    • Expected Outcome: Sentiment analysis model, visualizations of sentiment trends, identification of influential topics or sentiment shifts.
  5. Health Analytics for Disease Diagnosis:
    • Objective: To develop a machine learning model that aids in diagnosing diseases based on patient symptoms, medical history, and test results, facilitating accurate and timely diagnoses.
    • Tools and Technologies: Python (Pandas, Scikit-learn, TensorFlow), medical datasets, machine learning algorithms (classification), data preprocessing techniques
    • Expected Outcome: Disease diagnosis model, accuracy assessment, improved diagnostic decision-making.
  6. Recommender System for Movie or Product Recommendations:
    • Objective: To build a personalized recommender system that suggests movies or products to users based on their preferences and behavior, enhancing user experience and driving customer engagement.
    • Tools and Technologies: Python (Pandas, Scikit-learn), collaborative filtering techniques, recommendation algorithms, web scraping (for product data)
    • Expected Outcome: Recommender system, personalized recommendations, improved user engagement and satisfaction.



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    From Data to Decision: Excel in Data Science and Big Data Technologies
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