Data Science Fundamentals - Learning Basic Concepts, Data Wrangling, and Databases with Python

Colleagues, in the Data Science Fundamentals - Learning Basic Concepts, Data Wrangling, and Databases with Python: 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. Learn to get up and running with a Python data science environment, the essentials of Python 3, including object-oriented programming, The basics of the data science process and what each step entails, How to build a simple (yet powerful) recommendation engine for Airbnb listings, Where to find high-quality data sources and how to scrape websites if no existing dataset is available, How to work with APIs programmatically, including (but not limited to) the Foursquare API, Strategies for parsing JSON and XML into a structured form, How to build data models and work with database schemas, The basics of relational databases with SQLite and how to use an ORM to interface with them in Python, Best practices of data validation, including common data quality checks, How to query data in a database, including joining data tables and aggregating data, The fundamentals of exploratory data analysis, How to find and handle missing or malformed data, and The importance of creating reproducible analyses and how to share them effectively.

Enroll today (teams & executives are welcome): https://tinyurl.com/4yckspdc 

Download your free Data Science - Career Transformation Guide.

For your listening-reading pleasure:

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)

Big Data Hadoop Certification

Colleagues in the Big Data Hadoop Certification: Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write. Ecosystem tools including HDFS, YARN, MapReduce, Hive and Pig. Throughout this online instructor-led live Big Data Hadoop certification training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism, and Finance domains using Edureka's Cloud Lab. Edureka's Big Data Course provides you outstanding professional training with industry-based Projects work to clear Cloudera CCA 175 Certification exam on the first attempt. Skill-based training modules include: 1) Understanding Hadoop Certification, 2) Hadoop Architecture and HDFS, 3) Hadoop MapReduce Framework, 4) Advanced Hadoop MapReduce, 5) Apache Pig, 6) Apache Hive, 7) Advanced Apache Hive and HBase, 8) Advanced Apache HBase, 9) Processing Distributed Data with Apache Spark, 10) Oozie and Hadoop Project, and 11) Certification Project.

Enroll today (teams & executives are welcome): https://fxo.co/ARUw 

Download your free Data Science - Career Transformation Guide.

For your listening-reading pleasure:

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)

Applied Data Scientist with Python

Colleagues, in the Applied Data Scientist with Python training you will learn to solve data science problems, software and data engineering for  data scientists, experiment design and recommendations, data science project. Skill-based training modules with hands-on project include: 1) Solving Data Science Problems - learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders (Project: Write a Data Science Blog Post), 2) Software Engineering for Data Scientists - Develop software engineering skills that are essential for data scientists, such as creating unit tests and building classes, 3) Data Engineering for Data Scientists - learn to work with data through the entire data science process, from running pipelines, transforming data, building models, and deploying solutions to the cloud (Project: Build Disaster Response Pipelines with Figure Eight), 4) Experiment Design and Recommendations - Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems (Projects: Design a Recommendation Engine with IBM), and 5) Data Science Projects - leverage what you’ve learned throughout the program to build your own open-ended Data Science project. This project will serve as a demonstration of your valuable abilities as a Data Scientist (Project: Data Science Capstone Project). 

Enroll today (teams & executives are welcome): https://tinyurl.com/2smrvvvu 

Download your free Data Science - Career Transformation Guide.

For your listening-reading pleasure:

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)

Advanced Predictive Modeling in R Certification

Colleagues, in the Advanced Predictive Modeling in R Certification uou will learn Ordinary Least square regression, advanced regression, imputation, dimensionality reduction, correlation and linear regression analysis. This Certification Training is intended for a broad audience as both, an introduction to predictive models as well as a guide to applying them, covering topics such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction etc. Readers will also be able to learn basics of Statistics, such as Correlation and Linear Regression Analysis. 

Enroll today (teams & executives are welcome): https://fxo.co/ARUs 

Download your free Data Science - Career Transformation Guide.

For your listening-reading pleasure:

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)

Data Science Course: Complete Data Science Bootcamp

Colleagues, in the “Data Science Course: Complete Data Science Bootcamp” you will learn how to pre-process data, understand the mathematics behind Machine Learning, start coding in Python and learn how to use it for statistical analysis, perform linear and logistic regressions in Python, carry out cluster and factor analysis, create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn, apply your skills to real-life business cases, use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data, unfold the power of deep neural networks, and improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance [66 sections • 520 lectures • 31h 46m total length]. Skill-based lessons include: 1) The Various Data Science Disciplines, 2) Popular Data Science Techniques, 3) Probability - Combinatorics, Bayesian Inference, Distributions, 4) Statistics - Descriptive Statistics, Inferential Statistics Fundamentals, Hypothesis testing, 5) Python - Variables, Data Types, Syntax, Operator, Conditional Statements, Functions, Sequences, Iterations, 6) Deep Learning - How to Build a Neural Network from Scratch with NumPy, TensorFlow, NNs, Deep Neural Networks, Overfitting, Initialization, and 7 Case studies.

Enroll today (teams & executives are welcome): https://tinyurl.com/3fuaecaj 

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) https://tinyurl.com/hh7bf4m9 

Foundations: Data, Data, Everywhere (Google certification program)

Colleagues, the “Foundations: Data, Data, Everywhere” training program is part of Google Data Analytics Professional Certificate. 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. Describe the role of a data analyst with specific reference to jobs. Acquire highly marketable skills in Spreadsheet, Data Analysis, SQL, Data Visualization and Data Cleansing. 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. 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.

Enroll today (teams & execs welcome): https://imp.i384100.net/y2nZDB 

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Tools for Data Science (IBM)

Colleagues, the “Tools for Data Science” program from IBM will equip you to describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools, utilize languages commonly used by data scientists like Python, R, and SQL, demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features, and create and manage source code for data science using Git repositories and GitHub. Gain high-demand skills in Data Science, Python Programming, Github, Rstudio and Jupyter notebooks. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations.  Skill-based training lessons include: 1) Overview of Data Science Tools, 2) Languages of Data Science, 3) Packages, APIs, Data Sets, and Models, 4) Jupyter Notebooks and JupyterLab, 5) RStudio & GitHub, 6) Create and Share your Jupyter Notebook, and 7)  IBM Watson Studio. 

Enroll today (teams & executives are welcome): http://imp.i384100.net/21Gr3A 

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)

3 - The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age (Audible) (Kindle) 

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