Data Science Full Course - Complete Data Science Course | Data Science Full Course For Beginners IBM

Introduces the field of data science, its growth, and relevance in the industry. Discusses the median salary, career opportunities, and the transformation of organizations using data.

Details the key technologies for business in the context of data science, focusing on IBM AI foundations for business and instructional videos with professionals.

Dives into foundational concepts, key tools, data mining techniques, and artificial intelligence concepts like machine learning in the data science field.

Explores optional modules and concepts such as data literacy, databases, data warehouses, data marts, and data lakes for practical application.

Explores the process of using data to uncover insights, make strategic decisions, and transform information into impactful stories.

Discusses the definition of data science, its historical context, and the importance of curiosity, argumentation, and communication skills for data scientists.

Looks at the evolving landscape of data science, the role of data scientists in various industries, and the impact of digital transformation on businesses.

Provides an introduction to cloud computing, including its characteristics, deployment models, service models, and the benefits of leveraging cloud resources.

Defines big data, discusses the V's of big data (velocity, volume, variety, veracity, and value), and explores examples and challenges of big data analytics.

Traces the evolution of data science, trends in the field, the intersection of technology and analytics, and the significance of data in decision-making processes across industries.

Introduction to Hadoop, a Java-based open-source framework for distributed storage and computing with features such as scalability and fault tolerance.

Hadoop provides a reliable, scalable, and cost-effective solution for storing various types of data like audio, video, social media, and clickstream data, with self-service access and fault tolerance.

Explanation of the Hadoop architecture, which involves multiple commodity hardware connected through a network, splitting large files across multiple computers, and replicating file blocks on different nodes for fault tolerance.

Description of Hadoop Distributed File System (HDFS) and Apache Hive, a data warehouse software for reading systems intended for long sequential scans and data warehousing tasks.

Overview of Spark, a general-purpose data processing engine designed for interactive analytics, stream processing, machine learning, and significantly increasing the speed of computations using Python, R, and SQL.

Overview of generative AI, its applications in creating content like images and music, and its significance in the field of data science for generating synthetic data and enhancing analytics.

Explanation of neural networks in machine learning, their training process, and their applications in recognizing speech, objects, and patterns.

Discussion on the essential skills required for data scientists, including mathematics, programming, statistics, database knowledge, and computational thinking.

Encouraging individuals interested in data science to pursue it as a career due to its high demand and the opportunity to help companies grow.

Overview of careers and recruiting in data science, explaining the diverse backgrounds of data scientists and the skills required beyond technical abilities, such as presenting and storytelling.

Key skills and qualities companies should look for in data scientists, including excitement about working with data, industry relevance, analytical and computational thinking, computer programming, and data visualization.

Explanation of different data types including structured, semi-structured, and unstructured data, with examples and sources (such as databases, XML, JSON, and various file formats like images, videos, documents, and more).

Introduction to common data sources like relational databases, flat files, and XML data sets, discussing their use in organizations and data analysis.

Explanation of data storage and retrieval systems, focusing on data repositories like relational databases, NoSQL databases, and Big Data repositories, and their importance in analyzing and managing data effectively.

Overview of ETL (Extract, Transform, Load) process and data pipelines in data integration, detailing the steps involved, tools used, and the role of ETL in converting raw data into analysis-ready data.

Explanation of different database types including relational databases like RDBMS, non-relational databases (NoSQL), and various database models like document-based, column-based, and graph-based databases, highlighting their characteristics and use cases.

Overview of data integration, its importance for organizations in managing and delivering data for analytics, and the significance of data literacy for data scientists in understanding storage possibilities and making discoveries in data.

Introduction to data science tools and training modules covering languages, programming, APIs, machine learning, visualizations, GitHub, and project creation, providing a roadmap for learners interested in becoming data scientists.

Overview of the tasks that a data scientist needs to perform, including data management, data integration, transformation, and data visualization.

Explanation of data management, data integration, data transformation, and data visualization tasks in data science, including processes such as ETL, data extraction, transformation, loading, data visualization, and data asset management.

Description of model building tasks, including training data, analyzing patterns with machine learning, and making predictions on unseen data.

Explanation of model deployment, integrating developed models into applications, continuous quality checks, and model monitoring using tools like IBM Watson Open Scale.

Overview of code asset management tasks in data science, including version control, bug fixing, improving code features, and organizing data properly.

Description of execution environments for data science tasks, using tools like IBM Watson Studio and IBM Cognos Dashboard Embedded.

Listing of open source data management tools such as relational databases like MySQL, PostgreSQL, NoSQL tools, Hadoop file system, and cloud file systems.

Overview of data integration and transformation tools like Apache Airflow, Cubeflow, Apache Kafka, Apache Nifi, and Apache Superset.

Explanation of widely used open source data visualization tools, including Matplotlib, Seaborn, Bokeh, and Plotly, for creating visual representations of data.

Introduction to processing and exploratory analysis using notebooks in the project datasets on Dax, including basic and advanced walkthroughs for developers. Overview of IBM Data Asset Exchange (Dax) site providing preview data sets and notebooks on Dax. Explanation of machine learning models and the process of learning from models to make predictions.

Explanation of using machine learning models to solve problems by utilizing data containing valuable information. Overview of training models to identify patterns in data for making predictions. Discussion on supervised learning, unsupervised learning, reinforcement learning, and deep learning as specialized types of machine learning.

Overview of the Model Asset Exchange (Max) for deploying deep learning models efficiently. Explanation of training models from scratch or utilizing pre-trained models to reduce time to value. Details on creating model serving microservices for rapidly deploying models in local and cloud environments.

Introduction to Jupyter notebooks and Jupyter Lab, browser-based applications for accessing and working with multiple notebooks, text editors, terminals, and various file formats. Explanation of the functionalities and usage of Jupyter notebooks in data science projects.

Explanation of Jupyter kernels as computational engines for executing code in notebook files. Details on launching kernels, installing additional languages, and setting up Jupyter environments for data science projects.

Overview of Jupyter architecture with a two-process model involving kernels and clients. Explanation of how kernels execute code in notebook documents and the role of the notebook server in saving, loading, and converting files.

Introduction to Anaconda Jupiter environments and tools for combining code, explanatory text, and multimedia resources in a single document. Details on Anaconda Navigator, platforms, and libraries for data processing and machine learning.

Introduction to R programming language for data processing, manipulation, and statistical inference. Overview of using R Studio for coding, visualizations, and handling data analysis tasks. Description of R data visualization packages and plotting functions.

Overview of Git and GitHub as version control systems for managing code, tracking changes, and collaborating on software projects. Explanation of branches, merges, pull requests, and repository management in Git and GitHub environments.

Introduction to Watson Studio as a collaborative platform for data science tasks, including creating projects, managing machine learning models, and using notebooks and scripts. Overview of Cloud Pack for Data and services available in Watson Studio.

Learn how to create an IBM Cloud account and a Watson Studio account, set up a project, and define user details and settings.

Explore how to create, share, and run Jupiter notebooks in Watson Studio. Learn about setting up code editors, specifying runtime environments, and uploading and analyzing data.

Discover different Jupiter notebook templates, changing the kernel, creating, and executing notebooks using various tools and environments in Watson Studio.

Learn how to connect a Watson Studio account with GitHub, create access tokens, integrate repositories, and publish and push notebooks to GitHub for collaboration and version control.

Understand the 10 stages of standard data science methodology, including data collection, preparation, modeling, evaluation, and deployment. Learn the key questions answered in each stage.

Delve into data science methodology discussions focusing on business understanding, data collection, understanding, and preparation. Explore the importance of following a structured methodology in data science projects.

Learn about the importance of defining data sources, collecting initial data, and assessing and filling missing data. Understand the significance of data collection in data science projects and its impact on subsequent stages.

Explore the process of collecting data from various sources, including provider records and information needed to build predictive models. Understand the critical role of data collection in data science projects and decision-making.

Discover the data preparation phase in data science projects, similar to washing and cleaning ingredients before cooking. Learn the significance of preparing data for effective analysis and modeling.

Learn about the process of data preparation, including handling missing values, transforming data, and creating features that are essential for predictive modeling. Understand the crucial role of data preparation in data science projects.

Explore the modeling and evaluation stages in data science methodology, including building predictive models, tuning parameters, assessing model accuracy, and refining models based on feedback. Understand the significance of modeling in data science projects.

Understand the deployment and feedback stages in data science projects, focusing on implementing models, monitoring outcomes, measuring impact, refining models based on feedback, and ensuring continuous improvement. Learn about the cyclical nature of data science methodology.

Discover the importance of storytelling in data analysis, emphasizing the need to communicate insights effectively through compelling narratives. Understand the role of storytelling in conveying complex data to different stakeholders and driving actionable decisions.

In this chapter, you have learned how to think like a data scientist with real-world examples. It covers various steps from forming a concrete business or research feedback after model deployment to moving from problem to approach effectively.

This chapter focuses on the methodical ways of moving from problem to approach in data science. It covers selecting the most effective analytic approach to answer questions, understanding and preparing data for modeling, and evaluating models.

Here, you learn about data science methodology and the iterative nature of the stages involved. It includes a real case study on business requirements, methodology for reporting functions, and the success of a new pilot program.

This chapter introduces Crisp DM (Cross-Industry Standard Process for Data Mining) and its structured approach for data mining projects. It explains the six stages of Crisp DM methodology and its flexibility at each stage.

The chapter provides an overview of Python as a programming language, highlighting its ease of learning and versatility for data analysis, web scraping, working with big data, and more.

This chapter covers the fundamentals of Python programming, including expressions, variables, operations, conditions, branching, loops, and functions. It introduces popular libraries like numpy and pandas.

Here, you learn about the benefits of using Python, such as its wide adoption in the data science field and its applications in various areas, including artificial intelligence, machine learning, and data processing.

This chapter provides a guide on running, inserting, and shutting down notebook sessions in Jupyter. It explains how to work with multiple notebooks, create markdown for presentations, and manage notebook sessions effectively.

In this chapter, you explore different data types in Python, including integers, floats, strings, booleans, tupples, lists, dictionaries, and sets. It covers examples and operations specific to each data type.

This chapter delves into comparison operations in Python, focusing on equality, greater than, less than, and not equal operations. It explains how to apply these operations to numbers, strings, and boolean values.

Here, you learn about defining and using functions in Python. It covers creating custom functions, passing inputs, and returning outputs. The chapter emphasizes the importance of documenting functions for clarity and usability.

Introduction to the concept of functions in programming and how they operate with different data types to perform operations.

Explaining the basics of exception handling in programming, including error messages, error handling, and avoiding program termination due to errors.

Exploring objects and classes in Python, including creating instances of a class, defining attributes, and methods.

Discussing file handling in Python, including opening, reading, writing, and closing files using built-in functions.

Covering array operations using numpy library in Python, including array creation, indexing, slicing, and basic operations.

Explaining matrix operations in numpy, such as matrix addition, multiplication, and dot product.

Introducing APIs, HTTP protocol, libraries like 'requests' for working with HTTP, and different HTTP methods (GET, POST).

Learn how to extract information from web pages using Python and HTML, understand the structure of HTML and how to navigate through it to extract desired data, and explore web scraping techniques.

Discover how Python libraries like Beautiful Soup can be used to parse HTML documents, extract data, and navigate through HTML trees efficiently.

Explore different web scraping techniques, including parsing web pages, filtering data using Beautiful Soup, and extracting information using Python.

Learn how to extract data from websites using Python, understand the requests library for downloading web pages, parse HTML content, and scrape web pages for valuable information.

Gain insights into using SQL for data science, understanding relational databases, and querying data using SQL statements.

Understand database fundamentals, relational database models, and entities and attributes in databases.

Explore cloud databases, database services, and the advantages of using cloud computing for database management.

Learn about SQL statements for interacting with relational databases, defining objects in a database, and using DDL and DML statements effectively.

Discover how to use string patterns in select statements to retrieve specific data from relational database tables based on patterns, ranges, and sets of values.

Learn advanced techniques for data retrieval, sorting data in ascending or descending order, and indicating the column to use for sorting in SQL queries.

Techniques for retrieving data from a relational database table, sorting, and grouping the result set.

Describing how to further restrict a result set to avoid duplicate values in select statements.

Explaining the use of the group by clause to group results into subsets with matching values for one or more columns.

Exploring aggregate functions like sum, average, maximum, and minimum for data analysis and computation in SQL.

Understanding scalar functions for numeric and string data manipulation in SQL queries.

Detailing date and time function usage in SQL databases for managing temporal data effectively.

Showcasing the power of subqueries for advanced query operations and nested select statements in SQL.

Demonstrating the use of joins, including inner joins and outer joins, in SQL queries for combining data from different tables.

Utilizing Python libraries and DB APIs for efficient database connectivity, data retrieval, and analysis.

Explaining the process of creating tables, loading data, and querying data in IBM DB2 using SQL commands and Python.

Using Python's Pandas library for data analysis, manipulation, and visualization with real-world data sets.

Tips and considerations for handling real-world data sets, including CSV file processing and querying data effectively in SQL databases.

Understanding how to interact with database tables and columns, retrieve metadata, and query properties in SQL databases like DB2.

Defining views and stored procedures for organizing and accessing data efficiently in SQL databases.

Explaining ACID transactions, commit, and rollback commands for ensuring data consistency and integrity in database operations.

Overview of the join operator and how it combines data from two tables in a database.

Explanation of primary keys, foreign keys, and how to gather data from multiple tables using joins.

Description of an inner join operation in SQL that combines rows from two or more tables based on a matching value in a common column.

Explanation of inner joins, outer joins, and full outer joins, including when to use each type.

Definition and syntax for a left outer join, along with an example using the borrower and loan tables.

Explanation and syntax for a right outer join, with a demonstration using the borrower and loan tables.

Description and syntax for a full outer join, including an example with borrower and loan data.

Introduction to data pre-processing, including tasks like data cleaning, normalization, and binning.

Strategies for handling missing values in a dataset, such as dropping rows or replacing missing values.

Exploration of data formatting to bring data into a common standard of expression for consistency.

Explanation of data normalization techniques like Min-Max Scaling and Standardization to ensure data values are in a consistent range.

Definition and implementation of data binning to group numerical values into larger categories for analysis.

Explanation of converting categorical variables into numerical values for statistical modeling, using the example of fuel types in a car dataset.

Overview of EDA techniques like descriptive statistics, visualizations (box plots, scatter plots), grouping, and correlation analysis.

This chapter delves into model development and multiple linear regression. It explains how models can predict and evaluate prices of used cars based on various features. It covers the concept of independent variables, noise, training points, and error evaluation.

This chapter focuses on polynomial regression and residual plots. It discusses the use of polynomials to capture complex relationships in data and the importance of evaluating the residuals for model accuracy. The chapter also explains how to interpret residual plots to assess model performance.

In this chapter, the concept of ridge regression and hyperparameter tuning is explored. It details how ridge regression helps mitigate overfitting in models with multiple independent variables. The chapter also explains the process of hyperparameter tuning using grid search to optimize model performance.

This chapter covers cross-validation and model evaluation techniques. It explains how to split data into training and testing sets for assessing model performance. The chapter introduces mean squared error, R-squared, and cross-validation as methods to evaluate and improve model accuracy.

This chapter highlights the significance of data visualization and best practices for creating effective visualizations. It emphasizes the role of visualization in uncovering insights, trends, and patterns in data. The chapter also discusses key practices such as simplicity, clear labeling, and audience consideration in visualization design.

Explores the importance of data visualization, key visualization tools, and popular plot libraries like matplotlib, pandas, Seaborn, folium, plotly, and Pi.

Provides an overview of Matplotlib, describing its architecture, layers, and functionality for creating plots and graphics.

Demonstrates how to create conventional visualization tools using the plot function in Matplotlib, focusing on line plots and how to use Jupiter Notebook for plotting.

Introduces area plots as a visualization technique to show the magnitude and proportion of multiple variables over time, similar to line plots with filled areas below the lines.

Defines histograms as a way to represent numeric data distribution in bins, explains histogram creation using Matplotlib, and addresses alignment issues with tick marks on the horizontal axis.

Describes bar charts as tools to compare variable values at a given point, shows how to create bar charts in Matplotlib, and highlights customization options like color highlighting for specific bars.

Explains pie charts as circular statistical graphics to illustrate numerical proportions, demonstrates pie chart creation in Matplotlib, and touches on customizations like explode and slice highlighting.

Introduces box plots as statistical representations of data distribution through visualizing five key dimensions, demonstrates box plot creation in Matplotlib, and interprets the insights derived from box plots.

Describes scatter plots as tools to analyze correlations between variables, explains scatter plot creation using Matplotlib, and demonstrates color highlighting and customization options.

Explores the direct plotting process using Matplotlib, including importing the library, handling arrays for plotting, customizing plots with labels, titles, limits, legends, and other visual enhancements.

Explores waffle charts as a visualization technique for categorical data representation and wordcloud for textual data visualization, highlighting use cases and implementation techniques.

Introduces Seaborn as a data visualization library for high-level plotting, demonstrates scatter plots and regression line creation in Seaborn, and showcases additional customization features like color and marker change.

Introduces Folium as a powerful geospatial data visualization library in Python for creating maps, explains map creation using latitude and longitude values, and showcases different map styles available in Folium.

Learn how to create different map styles using tiles and how to add markers to a map using Folium. Import Folium, create a world map centered around Canada, set zoom level, add markers representing locations, and use feature groups for marker clustering.

Understand the importance of markers on maps and how they enhance interactivity and add context. Learn to add markers using the Folium marker function, specify locations, and provide additional information with pop-ups.

Explore how to generate marker clusters to declutter maps with multiple markers. Learn to create a list of locations, add markers to feature groups, and use clustering features for a visually enhanced map display.

Discover what choropleth maps are and how they display thematic data with shaded or colored areas. Learn about using GeoJSON files for geospatial data representation in creating choropleth maps.

Get an overview of web-based dashboarding tools like Dash in Python. Understand how dashboards help visualize data and enable informed decision-making for businesses by centralizing data and generating interactive charts.

Explanation of simple linear regression, fitting a line through data, and finding the best parameters for the line to make predictions.

Description of how the fitting line works in linear regression, including the equations for the fit line and parameters like Theta 0 and Theta 1.

Explanation of residual error, mean squared error (MSE), and the objective of linear regression to minimize the MSE for finding the best fit line.

Discusses how to find the best parameters Theta 0 and Theta 1 and the optimization approaches like using mathematical formulas or optimization algorithms like gradient descent.

Introduction to multiple linear regression, predicting outcomes using multiple independent variables, and understanding the model with examples.

Introduction to decision trees, building decision trees using recursive algorithms, selecting predictive features, and splitting data based on attributes.

Overview of logistic regression for classification tasks, the significance of logistic regression, and its use cases in solving classification problems.

Explanation of logistic regression, its applications, and when to use it. Logistic regression is used for binary classification and multiclass classification.

Differences between linear regression and logistic regression in predicting continuous values and binary outcomes.

Details on training a logistic regression model, including initializing theta, calculating model output, updating parameters, and stopping criteria.

Explanation of the cost function in logistic regression, minimizing cost function using optimization approaches like gradient descent.

Overview of Support Vector Machines (SVM) and their applications in classification problems, especially when dealing with high-dimensional data.

Explanation of clustering algorithms, including K-means clustering, hierarchical clustering, and DBSCAN algorithm.

Discusses obtaining launch data through SpaceX's API, normalizing structured JSON data into a flat table, and cleaning data sets by handling null values and filtering data.

Explains the attributes of Falcon 9 launch data, including flight number, booster version, landing pad, and launch site coordinates.

Focuses on analyzing launch site geography and proximity using folium, creating a dashboard application with Python plotly Dash, and predicting first-stage landing success using a machine learning pipeline.

Introduces interactive visual analytics for exploring and manipulating data effectively, utilizing folium for launch site analysis, and building a dashboard application with plotly Dash.

Provides an overview of generative AI, its applications in data generation, and its role in overcoming data scarcity and bias in various industries.

Details the impact of generative AI across industries such as healthcare, finance, retail, and entertainment, highlighting its role in creating new data insights and solutions using deep learning algorithms.

Discusses the five phases of the data science life cycle and how generative AI provides innovative tools to enhance data analysis and generate insights.

Explains various generative AI models like GANs, VAEs, autoregressive models, and flow-based models, showcasing their strengths and applications in data science for text, images, and other data types.

Demonstrates the use of generative AI for data augmentation, creating synthetic data, and handling missing values, outliers, and data merging in the data preparation process.

Illustrates how generative AI enables querying databases through natural language, converting queries to SQL commands, and exploring complex database structures for data analysis and manipulation.

This chapter covers the process of running univariate analysis, generating pair plots, creating new polynomial features, exploring data attributes, and generating statistical analysis summaries. It also discusses the significance of using generative AI tools for data analysis and visualization.

This chapter focuses on generating machine learning models, creating correlation matrices, scatter plots, bar charts, heat maps, and box plots using generative AI tools. It also compares various generative AI tools for model development and deployment.

In this chapter, different generative AI techniques like variational autoencoders (VAEs) and mutual information neural networks (MINNs) are discussed for data distribution modeling, feature engineering, anomaly detection, and prediction. It highlights the application of generative AI in improving model interpretability and preventing overfitting.

This chapter covers ethical considerations in using generative AI, including data quality, model interpretability, and ethical practices. It emphasizes the need for responsible deployment of generative AI technologies to prevent biases, maintain transparency, and ensure data privacy compliance.

This chapter discusses the key challenges in using generative AI, including technical, organizational, and cultural issues. It highlights the importance of addressing these challenges through responsible deployment and specialized skills in AI and data science.

This chapter outlines essential skills required for data scientists, including statistical knowledge, programming proficiency in languages like Python and SQL, data preparation, and machine learning expertise. It also emphasizes the significance of continuous learning, hands-on experience, and soft skills like communication and problem-solving.

This chapter explores various career paths in data science, including roles like data analyst, data scientist, data engineer, and AI engineer. It highlights the diverse opportunities in the data ecosystem and the skills needed for each role based on different company sizes and industries.

This chapter discusses the current trends in data science, including the growing demand for data professionals, the rise of data science jobs globally, and the importance of specialized skills like programming, statistical analysis, and machine learning. It also covers the opportunities and challenges in the data science field.

This chapter provides insights on building a strong data science portfolio, including showcasing projects, skills, and experiences. It emphasizes the importance of updating the portfolio with diverse projects that demonstrate technical proficiency, problem-solving abilities, and effective communication of results.

Exploring how to offer unique value to stakeholders by articulating and communicating the value proposition of your solution.

Tips on creating a data science portfolio, including leveraging past experiences and analysis, focusing on Python, SQL, and programming, and creating a code repository for collaborative problem-solving.

Details about a professional certification program in data science, including the courses, tools, projects, and duration, along with the benefits of earning the certificate.

Guidelines for drafting a professional resume, including structuring, content organization, highlighting skills and experience, and aligning the resume with job descriptions.

Tips for networking both online and offline, including staying updated on industry trends, utilizing job portals, connecting with professionals, and attending networking events.

Understanding the different types of job positions (full-time vs. contract), evaluating job requirements, and identifying warning signs in job listings.

Preparing for job interviews by developing an elevator pitch, practicing common interview questions, customizing questions for the company, and rehearsing with a friend for confidence.

Exploring the common steps in the interview process, factors impacting the process, and patterns of steps typically followed during interviews.

The video discusses completing code challenges for data scientist candidates, involving technical skills demonstrations for a company's interview process. It covers various types of interviews, including team, human resource screens, technical screens, and final interviews.

The chapter focuses on the interview process for data science candidates, including technical challenges, live coding tasks, self-presentation, and attitude. It also highlights the importance of problem-solving skills, excitement about the role, and avoiding phrases like 'I don't know' without context.

This chapter features an interview summary with Antonio Kanano, a Skills Network Engineering Manager and AI expert who focuses on the candidates' approach to answering questions rather than just the correctness of their answers.

The chapter describes Cindy's interview experience for the position of data scientist at IBM, emphasizing her technical knowledge, problem-solving skills, and the importance of a well-prepared resume and cover letter.

The section introduces coding challenges in data science, explaining the process, types of challenges, and expectations during technical screens. It also outlines the importance of clear instructions, problem-solving skills, and communication abilities during coding challenges.

This chapter covers tips for preparing for a final interview, including reviewing resume and cover letter, grooming, attire, and technical interview practices. It emphasizes the significance of clear communication, problem-solving explanations, and using the STAR method for behavioral interview questions.

Discussing the experience of setting a goal and failing, focusing on learning from the failure and ensuring it doesn't happen again.

Explaining the importance of preparing for behavioral interview questions, using the STAR method, and providing suggested answers for common behavioral questions.

Advising on appropriate questions to ask during an interview, such as inquiring about team dynamics and avoiding confrontational or personal questions.

Guidance on negotiating a job offer including identifying non-negotiables, valuing your worth, and preparing reasons for salary negotiations.