Python for Data Science and Machine Learning Bootcamp

Colleagues, in the “Python for Data Science and Machine Learning Bootcamp” class you will learn how to use NumPy, Pandas, Seaborn , Matplotlib, Plotly, Scikit-Learn, Machine Learning, and Tensorflow. In addition you will learn to use Python for Data Science, Machine Learning and Spark for Big Data Analysis, implement Machine Learning Algorithms, and use NumPy for Numerical Data, Pandas for Data Analysis, Matplotlib for Python Plotting, Seaborn for statistical plots, Plotly for interactive dynamic visualizations, SciKit-Learn for Machine Learning Tasks and K-Means Clustering. [165 lectures • 24 hours 54 minutes of training]. Training lessons address: 1) Environment Set-Up, 2) Jupyter Overview, 3) Python Crash Course, 4) Python for Data Analysis - NumPy, 5) Pandas, 6) Data Visualization - Matplotlib, Seaborn, 7) Pandas Built-in Data Visualization, Plotly and Cufflinks, 8) Geographical Plotting, 9) Data Capstone Project, 10) Introduction to Machine Learning, 11) Linear Regression, 12) Cross Validation and Bias-Variance Trade-Off, 13) Logistic Regression, 14) K Nearest Neighbors, 15) Decision Trees and Random Forests, 16) Support Vector Machines, 17) K Means Clustering, 18) Principal Component Analysis, 19) Recommender Systems, 20) Natural Language Processing, 21) Neural Nets and Deep Learning, and 22) Big Data and Spark with Python.

Enroll today (teams & executives are welcome): https://imp.i384100.net/75Azqr 

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 - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

R Programming A-Z™: R For Data Science With Real Exercises

Colleagues, in the “R Programming A-Z™: R For Data Science With Real Exercises” program you will learn programming in R And R Studio, Data Analytics, Data Science, Statistical Analysis, Packages, Functions, and GGPlot2. You will also learn the core principles of programming, how to create vectors in R and create variables, integer, double, logical, character and other types in R, create a while() loop and a for() loop in R, build and use matrices in R, the matrix() function, learn rbind() and cbind(), and how to install packages in R. [80 lectures • 10 hours of training]. Skill-based training modules address: 1) Hit The Ground Running, 2) Core Programming Principles, 3) Fundamentals Of R, 4) Matrices, 5) Data Frames, 6) Advanced Visualization With GGPlot2, and 7) Homework Solutions

Enroll today (teams & executives are welcome): https://imp.i384100.net/75Azqr 

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://datasciencecertificationtraining.blogspot.com/ 

Data Science Specialization

Colleagues, the “Data Science Specialization” program is a 10-course introduction to data science, developed and taught by leading professors. Use R to clean, analyze, and visualize data. Navigate the entire data science pipeline from data acquisition to publication. Use GitHub to manage data science projects. Perform regression analysis, least squares and inference using regression models. Acquire high-demand skills in Github, Machine Learning, R Programming and Regression Analysis. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. Skill-based training modules cover: 1) The Data Scientist’s Toolbox, 2) R Programming, 3) Getting and Cleaning Data, 4) Exploratory Data Analysis, 4) Reproducible Research, 5) Statistical Inference, 6) Regression Models, 7) Practical Machine Learning, 8) Developing Data Products and 9) Data Science Capstone.

Enroll today (teams & executives are welcome): https://imp.i384100.net/y20m12  

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)

Big Data Architect Masters Program

Colleagues, in the Big Data Architect Masters Program: The average salary earned by Big Data Architect is some $147,953 per annum. Develop a proficiency in tools and systems used by Big Data experts. This Big Data Engineer Course  includes training on Hadoop and Spark stack, Cassandra, Talend and Apache Kafka messaging system. The curriculum of our Big Data Engineering Courses has been determined by extensive research on 5000+ job descriptions across the globe. Edureka's comprehensive Big Data Engineer training course is designed by Industry top experts with real time project experience.  Training modules equip you in: 1) Java Essentials - training designed on one of the most popular platform ‘Java’, to train you on its basic concepts of Java and gain an entry into the programming world as a Java Developer, 2) Big Data Hadoop Certification Training- is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab, 3) Apache Spark and Scala Certification Training - prepares you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). You will get an in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX and Messaging System such as Kafka, 4) Apache Cassandra Certification Training - is designed by professionals as per the industry requirements and demands. This Cassandra Certification Training helps you to master the concepts of Apache Cassandra including Cassandra Architecture, its features, Cassandra Data Model, and its Administration. Throughout the Cassandra course, you will learn to install, configure, and monitor Cassandra, along with its integration with other Apache frameworks like Hadoop, Spark, and Kafka, and 5) Talend Certification Training For Big Data Integration - Designed to meet the industry benchmarks, Edureka’s  Talend certification training for Big Data is curated by top industry experts. This Talend certification course is created to help you learn how to use Talend Open Studio to simplify Big Data Integration. This Talend Data integration certification is live, instructor-led & helps you master key Talend concepts, with hands-on demonstrations. This course is fully immersive where you can learn and interact with the instructor and your peers. Enroll now in this Talend online course & be a professional Talend certified developer , and 6) Apache Kafka Certification Training Course - learn  the concepts about Kafka Architecture, Configuring Kafka Cluster, Kafka Producer, Kafka Consumer, Kafka Monitoring. The Apache Kafka training course is designed to provide insights into Integration of Kafka with Hadoop, Storm and Spark, understand Kafka Stream APIs, and implement Twitter Streaming with Kafka.

Enroll today (teams & executives are welcome): https://fxo.co/Esq4 

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)

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)