Data Science - Top 10 Countdown of Certification & Training Programs (#4)

Colleagues, #4 on our Top 10 countdown is Interpreting Data Using Statistical Models with Python. Gain the ability to go one step beyond visualizations and basic descriptive statistics, by harnessing the power of inferential statistics. First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, discover how the classic t-test can be used in a variety of common scenarios around estimating means. Also learn about related tests such as the Z-test, Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances. Finally, you will round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. Upon completion you will have the skills and knowledge to use powerful techniques from hypothesis testing, including t-tests, ANOVA and regression tests in order to measure the strength of statistical relationships within your data. {Pluralsight}

Enroll today (teams & execs welcome): https://tinyurl.com/22xntxa2 

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Data Science - Top 10 Countdown of Certification & Training Programs (#5)

Colleagues, according to Mordor Intelligence the global data science platform market was valued at $31B in 2020, and it is expected to reach $230B by 2026, registering a CAGR of 39.7 % during the forecast period. The #5 recommendation on our Top 10 countdown is Data Science with R from Pluralsight. Learn about the practice of data science, the R programming language, and how they can be used to transform data into actionable insight, how to transform and clean your data, create and interpret descriptive statistics, data visualizations, and statistical models, and how to handle Big Data, make predictions using machine learning algorithms, and deploy R to production. By the end of this course, you'll have the skills necessary to use R and the principles of data science to transform your data into actionable insight. Skill-based training modules include: 1) Introduction to Data Science, 2) Introduction to R, 3) Working with Data, 4) Creating Descriptive Statistics, 5) Creating Data Visualizations, 6) Creating Statistical Models, 7) Predicting with Machine Learning, and 8) Deploying to Production.

Enroll today (teams & execs welcome): https://tinyurl.com/mst76p4e 

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Top 10 Python Developer Certification & Training Programs (#7)

Colleagues, a Python Developer’s average salary in the US at $110,906 according to CareerFoundry. Number 7 on our Top 10 countdown is the Core Python 3: Advanced Flow Control program from Pluralsight.  This course will teach you extensions and alternatives to these basic structures that can help your code be easier to write and more likely to be correct. learn to apply alternative techniques for flow control. First, you’ll explore loop-else clauses. Next, you’ll discover try-else clauses. Finally, you’ll learn how to perform multi-way branching and leverage short-circuit evaluation. When you’re finished with this course, you’ll have the skills and knowledge of advanced Python flow control needed to create elegant, understandable, and fast programs. Training modules will equip you in: 1 - Loop-else Clauses, Version Check, Loop-else Clauses, The While-else Construct, Evaluating Stack Programs; 2 - For-else Clauses - Handling Search Failure With for-else, Refactoring from Loop-else to Extracted Functions; 3 - Try-else Clauses,  Try-else Clauses, Narrowing Try-block Scope Using try-else 2m; 4 - Emulating Switch, Emulating Switch, Refactoring from If-elif-else to Mappings of Callables, 4 - Dispatching on Type,  Refactoring to Separate Concerns, Dictionary Dispatch,  Introspective Lookup, The single dispatch Decorator,  Overloading Methods, Implementing Multiple Dispatch, 5 - Short-circuit Evaluation, The Logical-and Operator, The Logical-or Operator, Coalescing Nulls 6m, Guarding Expressions with Logical-and Safe Expressions with Shortcut Evaluation. 

Enroll today (teams & execs welcome): https://tinyurl.com/4px5ycea 

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Top 10 Machine Learning Certification & Training Programs (#7)

Colleagues, as reported by BuiltIn the average salary for a Machine Learning Engineer is $145,159. Number 7 on our Top 10 countdown is the Intro to Machine Learning with Tensorflow program from Udacity. Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects. Training modules - each with an hands-on project - include: 1) Supervised Learning - learn about supervised learning, a common class of methods for model construction (Project: Find Donors for CharityML - CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. Your goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent to ask for donations.), 2) Deep Learning - learn the foundations of neural network design and training in TensorFlow (Project: Create Your Own Image Classifier - As a machine learning engineer at a fictional self-driving car startup, you have been asked to help decide whether to build or buy an object detection algorithm for objects that may be on the side of the road. A company, Detectocorp, claims an 80% accuracy rate on the CIFAR-10 dataset, a benchmark used to evaluate the state of the art for computer vision systems. Use a neural network to recognize objects in images and evaluate the model's performance compared to Detect Corps model.), and 3) Unsupervised Learning - learn to implement unsupervised learning methods for different kinds of problem domains (Project: Create Customer Segments - determine if any similarities exist between customers and use those similarities to segment customers into distinct categories using various clustering techniques. This segmentation is used to help the business make more informed marketing and product decisions.). 

Enroll today (teams & execs welcome): https://tinyurl.com/42e8wsak 

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Top 10 Machine LearningCertification & Training Programs (#8)

Colleagues, the average salary for a Machine Learning Engineer is $145,159 as reported by BuiltIn.. Number 8 in our Top 10 Countdown is the Probability and Statistics for Machine Learning program from InformIT. Understand the appropriate variable type and probability distribution for representing a given class of data, Calculate all of the standard summary metrics for describing probability distributions, as well as the standard techniques for assessing the relationships between distributions, Apply information theory to quantify the proportion of valuable signal that's present among the noise of a given probability distribution, Hypothesize about and critically evaluate the inputs and outputs of machine learning algorithms using essential statistical tools such as the t-test, ANOVA, and R-squared, Grasp the fundamentals of both frequentist and Bayesian statistics, as well as appreciate when one of these approaches is appropriate for the problem you're solving, Use historical data to predict the future using regression models that take advantage of frequentist statistical theory (for smaller data sets) and modern machine learning theory (for larger data sets), including why we may want to consider applying deep learning to a given problem, and Develop a deep understanding of what's going on beneath the hood of predictive statistical models and machine learning algorithms. Skill-based training modules cover: 1) Introduction to Probability, 2) Random Variables, 3) Describing Distributions, 4) Relationships Between Probabilities, 5) Distributions in Machine Learning, 6) Information Theory, 7) Introduction to Statistics, 8) Comparing Means, 9) Correlation, 10) Regression, and 11) Bayesian Statistics.

Enroll today (teams & execs welcome): https://tinyurl.com/3tcbp5rr 

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Data Science - Top 10 Countdown of Certification & Training Programs (#6)

Colleagues, according to Mordor Intelligence the global data science platform market was valued at $31B in 2020, and it is expected to reach $230B by 2026, registering a CAGR of 39.7 % during the forecast period Our #6 recommendation on our Top 10 countdown is Python for Data Science from InformIT.  Learn how to program for Data Science and Machine Learning with Python. This is the antidote to the over-complicated universe of these hot new, growing technologies. With this course, students will learn the fundamentals of Python and get prepared specifically for Data Science. Notebook-based Data Science programming in Python is the emerging standard but there is a dearth of quality training material available for beginners. This 9-hour video, complete with interactive quizzes, provides foundational training on the Python language for the novice or beginner programmer looking to start in the Data Science field. The video serves as the 100-level course for a Data Science undergraduate or graduate program. Skill-based training modules include: 1 - Python Past and Future, 2 - Introduction to Colab, 3 - Fundamentals of Python, 4 - Strings in Python, 5 - Python Data Structures, 6 - Data Conversion Recipes, 7 - Execution Control, 8 - Functions in Python, 9 - Data Science Libraries, 10 - Functional Programming, 11 - Lazy Evaluation, 12 - Pattern Matching, 13 - Sorting in Python, 14 -  I/O in Python, 15 - Sharing Your Work, and 16 - Case Studies.

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

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Top 10 Machine LearningCertification & Training Programs (#9)

Colleagues, according to Glassdoor the average salary for a Machine Learning Engineer is $123,524. Number 9 in our Top 10 Countdown is the AWS Machine Learning Engineer program from Udacity. Master the skills necessary to become a successful ML engineer. The skill-based training modules - each with a hands-on project - include: 1) Introduction to Machine Learning - begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon (Project: Predict Bike Sharing Demand with AutoGluon); 2) Developing Your First ML Workflow - create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline (Project: Build an ML Workflow on SageMaker); 3) Deep Learning Topics within Computer Vision and NLP - train, finetune, and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and which tools are used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks (Project: Image Classification using AWS SageMaker); 4) Operationalizing Machine Learning Projects on SageMaker - deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs and how to work with especially large datasets (Project: Operationalizing an AWS ML Project); and 5) Capstone Project: Inventory Monitoring at Distribution Centers - to build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. 

Enroll today (teams & execs welcome): https://tinyurl.com/43cfpu39 

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Data Science - Top 10 Countdown of Certification & Training Programs (#7)

Colleagues, after building your data wrangling and Python skills, our #7 recommendations on our Top 10 Countdown is Data Science Fundamentals Part 2 - Machine Learning and Statistical Analysis. This intermediate-level program will equip you in how to get up and running with a Python data science environment, the basics of the data science process and what each step entails, how (and why) to perform exploratory data analysis in Python with the pandas library, the theory of statistical estimation to make inferences from your data and test hypotheses, the fundamentals of probability and how to use scipy to work with distributions in Python, how to build and evaluate machine learning models with scikit-learn, the basics of data visualization and how to communicate your results effectively and the importance of creating reproducible analyses and how to share them effectively. Training modules include: 1) Exploring Data–Analysis and Visualization, 2) Making Inferences–Statistical Estimation and Evaluation, 3) Statistical Modeling and Machine Learning. The program concludes by discussing the differences between and nuances of statistics, modeling, and machine learning. I provide an overview of the various types of models and algorithms used for machine learning and introduce how to leverage scikit-learn–a robust machine learning library in Python–to make predictions.

Enroll today (teams & execs welcome): https://tinyurl.com/2rhu873h 

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Data Science - Top 10 Countdown of Certification & Training Programs (#8)

Colleagues #8 on our Top 10 Countdown of data science programs is Data Science Fundamentals Part 1 - Learning Basic Concepts, Data Wrangling, and Databases with Python. It focuses on the fundamentals of acquiring, parsing, validating, and wrangling data with Python and its associated ecosystem of libraries. After an introduction to Data Science as a field and a primer on the Python programming language, you walk through the data science process by building a simple recommendation system. After this introduction, you dive deeper into each of the specific steps involved in the first half of the data science process–mainly how to acquire, transform, and store data (often referred to as an ETL pipeline). You learn how to download data that is openly accessible on the Internet by working with APIs and websites, and how to parse this XML and JSON data. With this structured data, you learn how to build data models, store and query data, and work with relational databases. Along the way, you learn the fundamentals of programming with Python (including object-oriented programming and the standard library) as well as the best practices of building sustainable data science applications. Skill-based training modules address: 1: Introduction to Data Science with Python, 2: The Data Science Process–Building Your First Application, 3: Acquiring Data–Sources and Methods, 4: Adding Structure–Parsing Data and Data Models, 5: Storing Data–Persistence with Relational Databases, and 6: Validating Data–Provenance and Quality Control. 

Enroll today (teams & execs welcome): https://tinyurl.com/bc9k764e 

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