Automate Data Pipelines (training)

Colleagues, in the Automate Data Pipelines: Learn to build pipelines leveraging Airflow DAGs to organize your tasks along with AWS resources such as S3 and Redshift. Gain high-demand skills including Automate Data Pipelines, Apache Airflow, Data pipeline dags, Data pipeline partitioning, Amazon s3, Data pipeline maintenance,  Redshift, Data pipeline creation and Data lineage. Lessons: 1) Introduction to Automating Data Pipelines. Welcome to Automating Data Pipelines. In this lesson, you'll be introduced to the topic, prerequisites for the course, and the environment and tools you'll be using to build data pipelines, 2) Data Pipelines - learn the components of a data pipeline including Directed Acyclic Graphs (DAGs). You'll practice creating data pipelines with DAGs and Apache Airflow, 3) Airflow and AWS - create connections between Airflow and AWS first by creating credentials, then copying S3 data, leveraging connections and hooks, and building S3 data to the Redshift DAG, 4) Data Quality - track data lineage and set up data pipeline schedules, partition data to optimize pipelines, investigating Data Quality issues, and write tests to ensure data quality, 5) Production Data Pipelines - learn how to build Pipelines with maintainability and reusability in mind. They will also learn about pipeline monitoring, and 6) Hands-on Project: Data Pipelines - work on a music streaming company’s data infrastructure by creating and automating a set of data pipelines with Airflow, monitoring and debugging production pipelines.

Enroll today (teams & executives are welcome): https://imp.i115008.net/4P0JMn 

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 Fundamentals - Machine Learning and Statistical Analysis

Colleagues, in the Data Science Fundamentals - Machine Learning and Statistical Analysis: This training program will equip you to analyze a number of datasets from the wild, build a handful of applications, and apply machine learning algorithms in meaningful ways to get real results. And all along the way you learn the best practices and computational techniques used by professional data scientists. You get hands-on experience with the PyData ecosystem by manipulating and modeling data. You explore and transform data with the pandas library, perform statistical analysis with SciPy and NumPy, build regression models with statsmodels, and train machine learning algorithms with scikit-learn. All throughout the course you learn to test your assumptions and models by engaging in rigorous validation. Finally, you learn how to share your results through effective data visualization. Learn 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. 

Enroll today (teams & executives are welcome): https://tinyurl.com/2vu4jm44 

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 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