Data Science - Career Transformation Guide (2022 v2)

Colleagues, the new Data Science - Career Transformation Guide includes valuable resources that enable you to accelerate your career growth and income potential - Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Communities and Networking forums. This guide focuses on Data Science, Big Data, Analytics, Pandas and Jupyter Notebooks along with the tools and technologies to perform these functions including Python, R, Probability and Statistics, Google BigQuery, SQL, Hadoop and Tableau. The Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer $171,749 USD #1 out of all certifications. Indeed.com lists some 599,779+ Data Scientist positions in the United States alone. Our three-fold career advancement strategy is to Get Certified, Get Published and Get Connected. Success awaits you.

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Python, R, TensorFlow & PyTorch - Career Transformation Guide

Colleagues, the new Python, R, TensorFlow and PyTorch  - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities. The Certification and Training programs are categorized by Python, R, TensorFlow and PyTorch. The average salary for Python Developers in the US is $111,225 according to CareerFoundry. Salary.com reports the media salary for Python Developers across the US is $95,120 USD. For Python Developers with formal training and expertise in TensorFlow and/or PyTorch mathematical libraries AIA projects an additional 3%-5%+ positive income delta is quite realistic. Therefore, the Artificial Intelligence Academy highly recommends that Python Developers also acquire TensorFlow and/or PyTorch training and certification. Analytics Insight project demand for Python developers to rank #1 among all programming languages in 2023.

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Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide

Colleagues, our updated Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities. The Certification and Training programs are categorized by Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing and Computer Vision. Glassdoor estimates the average salary for a Machine Learning Engineer at $131,001 USD. Indeed lists 2091  openings with an averMachine Learning Engineer age nationwide salary of $131,276 USD. The San Francisco Bay Area is the high-end of the salary range at $193,485 with Eden Prairie, Minnesota at $106,780. SimplyHired now has 868,669 Computer Vision career listings alone. Seize the opportunity. Success awaits you!

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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}

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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.

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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. 

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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.). 

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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.

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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.

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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. 

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