Data Structures (training)

Colleagues, in the “Data Structures” training program you will acquire high-demand skills in Java, Graph Theory, Data Structures, Algorithms, C++ (Programming Language), Theoretical Computer Science, Debugging, C (Programming Language), and Programming Principles. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. This program will address key technical questions involving: What is a good strategy of resizing a dynamic array? How priority queues are implemented in C++, Java, and Python? How to implement a hash table so that the amortized running time of all operations is O(1) on average? What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space. Skill-based training modules focus on: 1) Basic Data Structures, 2) Dynamic Arrays and Amortized Analysis, 3) Priority Queues and Disjoint Sets, 4) Hash Tables, and 5) Binary Search Trees, and 6) Binary Search Trees 2.

Enroll today (teams & execs welcome): https://imp.i384100.net/e1Pnk6 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Foundations: Data, Data, Everywhere

Colleagues, the “Foundations: Data, Data, Everywhere” course is part of Google Data Analytics Professional Certificate. Learn to: Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems, Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking, Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics, and Describe the role of a data analyst with specific reference to jobs. You will gain high-demand skills involving Data Literacy, Data Processing, Data-Driven Decision-Making, SQL, Data Visualization, Query Languages, Exploratory Data Analysis, Data Cleansing, Data Visualization Software, Data Collection, Spreadsheet Software, Data Ethics, Business Analytics, Data Analysis, and Analytics. Skill-based training lessons address: 1) Introducing Data Analytics and Analytical Thinking, 2) The Wonderful World of Data, 3) Set Up Your Data Analytics Toolbox, and 4) Become a Fair and Impactful Data Professional. By the end of this course you will: Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job, Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox, Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process, Evaluate the role of analytics in the data ecosystem, Conduct an analytical thinking self-assessment, and Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.

Enroll today (teams & execs welcome): imp.i384100.net/je6dXn 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Data Structures, Algorithms and Machine Learning Optimization (training)

Colleagues, the “Data Structures, Algorithms, and Machine Learning Optimization” program provides you with a functional, hands-on understanding of the essential computer science for machine learning applications. Learn "big O" notation to characterize the time efficiency and space efficiency of a given algorithm,  use Python data structures, including list-, dictionary-, tree-, and graph-based structures, understand the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, implement statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, grasp versatile (stochastic) gradient descent optimization algorithm works, and familiarize yourself with the "fancy" optimizers that are available for advanced machine learning approaches. Skill-based training modules cover: 1) Orientation to Data Structures and Algorithms - Machine Learning Foundations Series, A Brief History of Data and Algorithms, and their Applications to Machine Learning; 2) "Big O" Notation - Constant, Linear and Polynomial  Time, Common Runtimes, Best versus Worst Case scenarios; 3) List-Based Data Structures - Lists, Arrays, Linked Lists, Doubly-Linked Lists, Stacks, Queues, Deques; 4) Searching and Sorting - Binary Search, Bubble-Merge-Quick Sorts; 5) Sets and Hashing - Maps and Dictionaries, Sets, Hash Functions, Collisions, Load Factor, Hash Maps, String Keys, Hashing in ML; 6) Trees - Decision Trees, Random Forests, XGBoost: Gradient-Boosted Trees; 7) Graphs - Directed versus Undirected Graphs, DAGs: Directed Acyclic Graphs, Pandas DataFrames; 8) Machine Learning Optimization - Statistics versus Machine Learning - Objective Functions, Mean Absolute Error, Mean Squared Error, Minimizing Cost with Gradient Descent, Gradient Descent from Scratch with PyTorch, Critical Points, Stochastic Gradient Descent, Learning Rate Scheduling, Maximizing Reward with Gradient Ascent; and 9) Fancy Deep Learning Optimizers - Jacobian Matrices, Second-Order Optimization and Hessians, Momentum, and Adaptive Optimizers.

Enroll today (teams & execs welcome): https://tinyurl.com/yc2dfb8f 

Much career success, Lawrence E. Wilson - Data Science Academy (subscribe & share)

Explore the “Data Driven Organizations” Amazon Audible & Kindle Book Series

Explore the “Data Driven Organizations” Amazon Audible & Kindle Book Series 

Data-Driven Organizations: 

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much success. Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

“The Promise of Data-Driven Decision Making - From Analytics to Visualization and Beyond” (audio & ebook)

Colleagues, “The Promise of Data-Driven Decision Making” (Audible) Kindle is a powerful resource that has the potential to transform the way we approach problem-solving in both our personal and professional lives. By collecting and analyzing data, we can gain valuable insights into the world around us, and use that knowledge to make more informed decisions. Throughout this book, we have explored the various aspects of data-based decision making, including the benefits, challenges, and best practices. We have also examined the different tools and techniques that can be used to collect and analyze data, as well as the ethical considerations that must be taken into account.

 Highly data-driven firms are three times more likely to report a major improvement in decision making, according to a large decision survey conducted by PWC. However, only one in three CEOs claim that their company is heavily data-driven. It comes up frequently in meetings with corporate leaders that executives have instant access to large volumes of data. We also learn that their personal intuition or gut feeling plays a significant role in their decision-making. How might the art and science of decision-making be combined better? A more efficient use of data and the capacity to draw insights are seen to present potential for enterprises to generate higher value. Analytics may support an organization's growth and innovation, increase productivity, and improve risk management when they are integrated into the culture of decision-making within the company. The use of facts, metrics, and data to inform strategic business decisions that are in line with a company's goals, objectives, and activities is known as data-driven decision-making. Interactive dashboards, work management platforms, and OKR tools are examples of modern analytics tools that assist individuals overcome prejudice and make the best management decisions that are in line with business strategies. Instead of making decisions based on intuition, opinion, or personal experience, it compiles historical data to examine trends and make better decisions for the future in relation to what has previously worked.

Listen today via Amazon Audible (https://tinyurl.com/ydbyh2t9) 

Or read now on Kindle (https://tinyurl.com/hptundzs) 

This book is part of the “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Data Scientist - 10 Best Practices, Portals & Career Development

Colleagues, this post will help you accelerate your career and income potential in the Data Science domain. Whether you are new to Data Science or or looking to advance your existing Data Scientist career this post has valuable information for you.

Best Practices

1. Master Core Foundations: Solidify your understanding of statistics, probability, linear algebra, and programming (Python/R), crucial for building robust models (Codecademy, Masters in Data Science).

2. Build a Strong Portfolio: Create diverse, impactful projects on platforms like Kaggle and GitHub, demonstrating end-to-end problem-solving and deployment skills.

3. Specialize Strategically: Focus on niches like NLP, computer vision, or MLOps, aligning with emerging industry demands (Hugging Face, OpenAI).

4. Embrace Cloud Platforms: Learn to deploy and manage data science solutions on major cloud providers (Google Cloud Data Science, Microsoft Azure AI).

5. Develop Data Visualization & Storytelling: Master tools like Power BI or Tableau to effectively communicate insights to non-technical stakeholders (Tableau.com).

6. Stay Current with Research: Regularly follow advancements in AI and ML from leading labs (DeepMind, OpenAI) and research publications (MIT Technology Review, X/Stats_Feed).

7. Practice MLOps: Understand the operational aspects of deploying, monitoring, and maintaining models in production environments.

8. Network and Collaborate: Engage with the data science community online (Kaggle forums, X) and in person to learn and find opportunities.

9. Pursue Lifelong Learning: The field changes constantly; commit to continuous education through courses, certifications, and hands-on experimentation.

10. Cultivate Business Acumen: Understand the business context and how data science solutions drive tangible value and impact organizational goals.

Resource Portals

• Coursera 

• Code Academy 

• Data Science on Google Cloud 

• Kaggle.com 

• Master’s in Data Science 

• Microsoft Power BI

• Tableau

• World of Statistics (X)

Specializations, Master Classes and Certifications

• Applied Data Science Specialization 

• Data Analytics Professional Certificate 

• Data Science: Foundations using R Specialization

• Data Science Fundamentals with Python and SQL Specialization

• Data Science Specialization

• Data Visualization with Tableau Specialization

• Data Science with Python and R

• Google Data Analytics Professional Certificate 

• IBM Data Analyst Professional Certificate

• IBM Data Science Professional Certificate

• Microsoft Power BI Data Analyst Professional Certificate (Exam PL-300) 

• Understanding Data Science 

Enroll today (teams & executives are welcome).  

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much success in your career journey, Lawrence E. Wilson - Data Science Academy (share with your team)

Data Science - Interviews, Project Portfolios & Certifications (Your competitive edge)

Colleagues, are you seeking to land the next job in your Data Science  journey … either within your current company or with a new employer? All hiring managers need to answer three fundamental questions in the interview process before making a job offer.

Interviews

1. Skills: Can you do the job?

2. Motivation: Will you do the job?

3. Fit: Will you be a team player and fit into the company culture?

Professional Portfolio

Creating a high qualityData Science professional portfolio combined with having industry-leading certifications - along with relevant work experience - will put you at the top of the candidate pool when answering the “Skills” question.:

What is a Project Portfolio? 

1. A project portfolio is a collection of projects, programs, and operations managed collectively to achieve strategic objectives. 

2. It demonstrates your diverse skills, experience, and proven ability to deliver results, showcasing your value and strategic impact to potential employers or for internal advancement.

What Makes a Project Portfolio Valuable?

1. Strategic Alignment: Connecting projects to organizational goals.

2. Resource Management: Efficient allocation of people and assets.

3. Risk Management: Identifying and mitigating potential threats.

4. Performance Monitoring: Tracking progress and outcomes.

5. Stakeholder Communication: Keeping all parties informed and engaged.

What are the best portals to host your project portfolio?

1. GitHub

2. LinkedIn

3. Behance

4. Dribbble

5. Kaggle

6. Hugging Face

7. Personal Website (WordPress, Wix, Squarespace, Webflow - Offers maximum customization & a professional brand presence)

Industry-Leading Certifications & Training

• Applied Data Science Specialization 

• Data Analytics Professional Certificate 

• Data Science: Foundations using R Specialization

• Data Science Fundamentals with Python and SQL Specialization

• Data Science Specialization

• Google Data Analytics Professional Certificate 

• IBM Data Analyst Professional Certificate

• IBM Data Science Professional Certificate

• Microsoft Power BI Data Analyst Professional Certificate (Exam PL-300) 

• Understanding Data Science 

Enroll today (teams & executives are welcome).  

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - Data Science Academy (share with your team)