eBook on Learning Data Science
Data Science is a multidisciplinary field that encompasses aspects of statistics, mathematics, computer science, and domain expertise to extract meaningful insights and knowledge from data. It involves the use of algorithms, methods, and systems to analyze large and complex datasets to uncover hidden patterns, correlations, and trends.
The primary goal of data science is to turn raw data into actionable insights that can inform decision-making and drive business value. To achieve this goal, data scientists use a combination of techniques from statistics, machine learning, and artificial intelligence, as well as domain-specific knowledge, to make sense of the data. They use these techniques to clean and preprocess the data, to build models that can make predictions or identify patterns, and to communicate their findings to stakeholders.
The eBook comes in PDF format and is priced at $9.99
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The book covers the following:
1 Introduction to Data Science
1.1 Definition of Data Science
1.2 Overview of Data Science Process
1.3 Importance of Data Science in today's world
2 Basic Statistics and Mathematics
2.1 Probability Theory
2.2 Descriptive Statistics
2.3 Inferential Statistics
2.4 Linear Algebra
2.5 Calculus
3 Data Exploration and Pre-processing
3.1 Data Collection
3.2 Data Cleaning
3.3 Data Transformation
3.4 Exploratory Data Analysis (EDA)
3.5 Data Visualization
4. Machine Learning
4.1. Types of Machine Learning like Supervised Learning, Unsupervised Learning and Reinforcement Learning
4.2 Basic concepts of Machine Learning like Bias and Variance, Overfitting and Underfitting, Model Selection, Cross-Validation
4.3 Popular Machine Learning Algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
5. Deep Learning
5.1 Overview of Neural Networks
5.2 Convolutional Neural Networks (CNN)
5.3 Recurrent Neural Networks (RNN)
5.4 Autoencoders
6 Big Data and Distributed Computing
6.1 Overview of Big Data
6.2 Distributed Computing Systems like Hadoop and Spark
6.3 NoSQL databases like MongoDB and Cassandra
7. Data Visualization and Communication
7.1 Data Visualization Tools like Matplotlib, Seaborn and Plotly
7.2 Data Communication including Data Storytelling and Data Presentation
8 Career Opportunities in Data Science
8.1 Overview of Data Science Careers
8.2 Job Titles
8.3 Skills Needed for Data Science Careers
8.4 Career Paths in Data Science
The eBook comes in PDF format and is priced at $9.99
Please click here to buy the book