Course in Data Science and Mining
100% Online
3 meses
260€
Matricularme

    Course in Data Science and Mining

    100% Online
    3 meses
    260€
    Matricularme
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    Presentación

    The Course in Data Science and Mining is your gateway to mastering one of the most sought-after skills in today's digital age. With data becoming the new oil, the demand for professionals who can analyse and interpret vast datasets has skyrocketed. This course offers you a comprehensive understanding of data science principles and mining techniques, equipping you with the ability to transform raw data into actionable insights. You'll learn to harness the power of data to drive decision-making and innovation across various industries. By participating in this online course, you'll position yourself at the forefront of a booming sector, gaining skills that are in high demand and essential for future-proofing your career. Join us to unlock new opportunities and become a leader in the dynamic field of data science.
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    Para qué te prepara
    The Course in Data Science and Mining equips you to tackle complex data challenges using advanced analytical techniques and tools. You'll be able to extract meaningful insights from vast datasets, apply machine learning algorithms, and develop predictive models. This course prepares you to solve real-world problems by transforming raw data into actionable knowledge, enhancing decision-making processes across diverse domains and industries.
    Objetivos
    - Understand data science principles and their real-world applications. - Develop skills to analyse datasets and extract insights effectively. - Learn to use statistical methods for data interpretation and analysis. - Master data visualisation techniques to present findings clearly. - Acquire knowledge of machine learning algorithms and their uses. - Explore data mining techniques to uncover hidden patterns. - Gain proficiency in using Python for data science tasks.
    A quién va dirigido
    The Course in Data Science and Mining is designed for professionals and graduates in the field keen on broadening or refreshing their expertise. Ideal for those interested in harnessing data-driven insights and exploring practical applications in their careers, this course offers a comprehensive yet accessible approach to contemporary data methodologies.
    Salidas Profesionales
    - Data Analyst in various industries - Business Intelligence Analyst - Machine Learning Engineer - Data Mining Specialist - Statistical Analyst - Predictive Modeller - Data Consultant - Operations Research Analyst - Data Engineer - Big Data Analyst - Marketing Analyst - Risk Analyst - Healthcare Data Analyst
    Temario

    UNIT 1. Introduction to Data Science

    1. What is data science?
    2. Tools Necessary for the Data Scientist
    3. Data Science & Cloud Computing
    4. Legal Issues in Data Protection

    UNIT 2. Relational databases

    1. Introduction
    2. The relational model
    3. Structured Query Language (SQL)
    4. 4.MySQL. A relational database

    UNIT 3. NOSQL Databases and Scalable Storage

    1. What is a NoSQL database?
    2. Relational Databases vs NoSQL Databases
    3. NoSQL Database Types: CAP Theorem
    4. NoSQL Database Systems

    UNIT 4. Introduction to a NoSQL Database system, MongoDB

    1. What is MongoDB?
    2. How to Operate and Use MongoDB
    3. First Steps with MongoDB: Installation and Shell Commands
    4. Creating our first NoSQL database: Model and data insertion
    5. Data Updates in MongoDB: Set and Update Statements
    6. Operating with Indexes in MongoDB for Data Optimisation
    7. Data query in MongoDB

    UNIT 5. WEKA and Data Mining

    1. What is WEKA?
    2. Data Mining Techniques in Weka
    3. Weka interfaces
    4. Attribute Selection

    UNIT 6. Python and data analysis

    1. Introduction to Python
    2. What Is Needed?
    3. Libraries for data analysis in Python
    4. MongoDB, Hadoop and Python: Big Data Dream Team

    UNIT 7. R as a tool for big data

    1. Introduction to R
    2. What Is Needed?
    3. Data types
    4. Descriptive and Predictive Statistics with R
    5. Integrating R into Hadoop

    UNIT 8. Data Preprocessing and Data Processing

    1. Data collection and cleaning (ETL)
    2. Statistical inference
    3. Regression models
    4. Hypothesis testing

    UNIT 9. Data analysis

    1. Business Analytical Intelligence
    2. Graph theory and social network analysis
    3. Presentation of results
    Titulación
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