Everand Logo

Welcome to Everand!

  • Discover millions of ebooks, audiobooks, and so much more with a free trial

    Only €10,99/month after trial. Cancel anytime.

    Data Science for Beginners
    Data Science for Beginners
    Data Science for Beginners
    Ebook69 pages54 minutes

    Data Science for Beginners

    By Tom Lesley

    ()

    Read preview

    About this ebook

    "Data Science for Beginners: A Practical Guide to Understanding and Applying the Fundamentals of Data Science" is an introduction to the exciting and rapidly growing field of data science. This comprehensive guide provides a clear and concise overview of the fundamentals of data science, including data collection, data analysis, data visualization, and machine learning.

     

    With a focus on practical applications, this book provides hands-on exercises, case studies, and real-world examples to help readers develop a deep understanding of data science concepts and techniques. Whether you are new to the field or simply looking to deepen your understanding of data science, this book provides an accessible and engaging introduction to the subject.

     

    Throughout the book, readers will learn how to collect and clean data, how to use various data analysis techniques, and how to visualize data to gain insights and make informed decisions. The book also covers advanced topics in data science, such as machine learning, and provides practical guidance on how to implement these techniques in real-world scenarios.

     

    With clear explanations and step-by-step instructions, this book is the perfect starting point for anyone looking to learn about data science and its applications. Whether you are a student, a professional, or simply someone who wants to understand this rapidly growing field, "Data Science for Beginners" is the essential guide to help you get started on your data science journey.

    LanguageEnglish
    PublisherMay Reads
    Release dateApr 19, 2024
    ISBN9798224854684
    Data Science for Beginners

    Read more from Tom Lesley

    Related to Data Science for Beginners

    Related ebooks

    Enterprise Applications For You

    View More
    View More

    Related podcast episodes

    Related articles

    • Article

      How To Make Sense From And With AI ?

      Sep 25, 2021

      How To Make Sense From And With AI ?
      4 min read

    • Article

      Why Your Organisation Needs To Lift Its Data Game

      Oct 22, 2019

      From problems stemming from the recent New Zealand census to data collected by Facebook, data has been in the news a lot lately. It may seem obvious that large organisations such as Statistics New Zealand and Facebook need to continually improve thei

      3 min read

    • Article

      Facilities Systems

      Oct 21, 2018

      Facilities Systems
      5 min read

    • Article

      Playing With Numbers

      Jul 18, 2019

      In the last few years, we have probably created more data digitally than in the rest of human history. Think about the millions of Internet searches and social media posts that are made every minute, and the resultant data that corporations and gover

      3 min read

    • Article

      PEOPLE ASSESSMENT in the Digital Age

      May 25, 2021

      PEOPLE ASSESSMENT in the Digital Age
      8 min read

    • Article

      Intelligence Analysis

      Jun 13, 2018

      Intelligence Analysis
      3 min read

    • Article

      Big Data, Broken Down

      Mar 26, 2019

      In simple terms, big data refers to huge amounts of data collected from various sources and analysed in real or near-real time. The data can be collected through wearable devices, social media, sensors, and other mobile devices. The data can be struc

      1 min read

    • Article

      Synthetic Data As A Double-Edged Sword In Africa's AI Revolution

      Sep 29, 2023

      Artificial intelligence (AI) is transforming companies and economies worldwide, including in Africa. Data is an essential component in the training of AI systems. Unfortunately, the lack of accurate, high-quality data is a significant impediment in A

      3 min read

    • Article

      Arnab PANDEY

      Apr 1, 2021

      Arnab PANDEY
      11 min read

    • Article

      Better Together: Behavioural Science + Data Science

      May 1, 2020

      IMAGINE THIS SCENARIO: You are designing a new customer experience to drive a shift in customer behaviour. You have reviewed the reports and dashboards describing current behaviour. You have asked customers how they felt and incorporated their feedba

      5 min read

    • Article

      A Method Of Foresight In The Field Of Strategy Innovation

      Feb 25, 2021

      This research consisted of two phases. Phase 1. Information gathering: systematic collection of relevant signals and trends, informed by academic knowledge and consulting company reports. Phase 2. Diagnosis is a three-step exercise: In-Depth Ana

      1 min read

    • Article

      Cognitive Enterprise

      Dec 1, 2021

      Cognitive Enterprise
      6 min read

    • Article

      The Future Of The Data Economy

      Jun 1, 2022

      The Future Of The Data Economy
      6 min read

    Related categories

    Reviews for Data Science for Beginners

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      Book preview

      Data Science for Beginners - Tom Lesley

      Tom Lesley

      Table of Content

      I. Introduction

      A. Definition of Data Science

      B. Importance of Data Science in today's world

      C. Overview of Data Science Process

      II. Fundamentals of Data Science

      A. Statistics

      1. Descriptive Statistics

      2. Inferential Statistics

      B. Probability

      1. Discrete Probability Distributions

      2. Continuous Probability Distributions

      C. Linear Algebra

      1. Matrix Operations

      2. Vectors

      3. Eigenvalues and Eigenvectors

      D. Calculus

      1. Limits

      2. Derivatives

      3. Integrals

      III. Data Collection and Preparation

      A. Types of Data

      1. Structured Data

      2. Unstructured Data

      3. Semi-structured Data

      B. Data Collection Methods

      1. Surveys

      2. Experiments

      3. Web Scraping

      C. Data Cleaning and Preparation

      1. Missing Values

      2. Outliers

      3. Data Transformation

      IV. Exploratory Data Analysis (EDA)

      A. Univariate Analysis

      1. Descriptive Statistics

      2. Visualizations

      B. Bivariate Analysis

      1. Correlation

      2. Visualizations

      C. Multivariate Analysis

      1. Dimensionality Reduction

      2. Clustering

      V. Predictive Modeling

      A. Overview of Predictive Modeling

      B. Regression

      1. Simple Linear Regression

      2. Multiple Linear Regression

      C. Classification

      1. Logistic Regression

      2. K-Nearest Neighbors (KNN)

      3. Support Vector Machines (SVM)

      D. Model Evaluation

      1. Confusion Matrix

      2. Accuracy

      3. Precision and Recall

      4. F1 Score

      5. ROC Curve

      I. Introduction

      A. Definition of Data Science

      Data Science is a multidisciplinary field that combines techniques from computer science, statistics, and domain expertise to extract insights and knowledge from data. The goal of data science is to turn raw data into actionable insights that can inform decision-making and drive business value.

      Data science encompasses a wide range of activities, including data acquisition, preprocessing, exploratory analysis, model building, deployment, and monitoring. The data science process typically begins with defining the problem and collecting relevant data. Next, the data is cleaned and preprocessed to ensure that it is in a format that can be analyzed.

      Once the data is ready, exploratory data analysis is performed to understand the patterns, relationships, and distributions in the data. This stage involves visualizing the data and using statistical techniques to identify any trends or anomalies.

      Based on the insights gained from the exploratory data analysis, a suitable model is selected and built. This can involve machine learning algorithms such as regression, decision trees, and neural networks, among others. The goal of this stage is to develop a model that accurately predicts the outcome of interest.

      Once the model has been built, it must be deployed and put into use. This typically involves integrating the model into a production environment and setting up processes to monitor its performance. It's also important to continually evaluate the model to ensure it remains accurate and relevant over time.

      Data science is a rapidly evolving field, and new technologies and techniques are being developed all the time. As such, data scientists must stay up-to-date with the latest developments in order to stay ahead of the curve.

      Data science is a field that leverages technology and statistical techniques to turn raw data into actionable insights that drive business value. It encompasses a wide range of activities and requires a multidisciplinary skill set, including computer science, statistics, and domain expertise.

      B. Importance of Data Science in today's world

      Data science has become increasingly important in today's world due to the vast amounts of data that are being generated on a daily basis. From social media interactions to financial transactions, there is a wealth of information available that can be used to inform decision-making and drive business value.

      One of the key benefits of data science is that it allows organizations to gain a deeper understanding of their customers, their operations, and the market. By analyzing large amounts of data, data scientists can identify patterns and relationships that would not be apparent through traditional methods of data analysis. This information can then be used to inform marketing strategies, improve operational efficiency, and identify new business opportunities.

      Another important aspect of data science is its ability to make predictions. Through the use of machine learning algorithms and other statistical techniques, data scientists can build models that can accurately predict future trends and outcomes. This information can be used to make informed decisions, such as determining the best times to launch a new product or adjusting pricing strategies.

      Data science is also playing an increasingly important role in addressing complex societal issues, such as climate change, healthcare, and crime. By analyzing large amounts of data, data scientists can identify patterns and relationships that can inform decision-making and drive change in these critical areas.

      In the business world, data science is becoming a critical competitive advantage. Organizations that are able to

      Enjoying the preview?
      Page 1 of 1