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

$15,990.00

[su_tabs mobile="desktop"] [su_tab title="Candidature" disabled="no" anchor="" url="" target="blank" class=""] 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…

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Description

“Unleashing the Power of Data for Transformative Insights”

 

Our comprehensive Data Science and Big Data Training Course is designed to equip participants with the knowledge, skills, and expertise to excel in the rapidly evolving field of data science. From the foundational concepts to advanced techniques, this course covers the complete spectrum of data science, enabling participants to become proficient in harnessing the power of data for transformative insights and decision-making. With a focus on hands-on learning and real-world applications, participants will work on multiple real-time projects throughout the course, solidifying their understanding and providing tangible outcomes to showcase their expertise.

Key Features:

  • Comprehensive curriculum covering the breadth and depth of data science and big data technologies
  • Industry-expert instructors providing in-depth guidance and mentorship
  • Hands-on exercises and real-time projects to reinforce learning and practical skills
  • Access to cutting-edge tools, software, and datasets used in the industry
  • Collaboration and networking opportunities with fellow data science enthusiasts
  • Continuous support and resources for post-training skill development and career advancement

Real-Time Projects: Participants will work on multiple real-time projects throughout the course, enabling them to apply their knowledge and skills to solve practical data science challenges. Each project will have clear objectives and expected outcomes, providing tangible results to showcase expertise.

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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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