Discover the Top 3 Data Science Certifications

Colleagues, here are our top 3 picks for the best Data Science certifications needed to advance your career and income potential. First up is the Google Data Analytics Professional Certificate. Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required. Next is the IBM Data Science Professional Certificate. You will prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. And third is the Data Scientist Certification from DataCamp. You will be equipped to collect, analyze and interpret large amounts of data using machine learning and AI when needed. You will also need to effectively communicate the results of your analysis to business stakeholders.

Enroll today (teams & executives are welcome). Let your DS journey begin:

Google Data Analytics Professional Certificate: (https://imp.i384100.net/Y9X74O) 

IBM Data Science Professional Certificate (https://imp.i384100.net/q4QYNn) 

Data Scientist Certification from DataCamp (https://datacamp.pxf.io/DKY71y)

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)

Microsoft Power BI Certification Training: PwC Academy

Colleague, the “Microsoft Power BI Certification Training: PwC Academy” offers dual certification in business intelligence. It covers concepts such as Power BI Desktop, DAX, Service, Reports, and more with real-world industry use cases. The training is live instructor-led and provides hands-on experience in real-time projects. It prepares you for the official PL-300 exam and offers simulated real-world scenarios. Enroll today to stand out in the job market with dual certification in the BI domain and advance your career. The global BI market has been on an impressive growth trajectory, starting at $27.11 B in 2022 and projected to reach $54.27 B by 2030, with a significant step up to $33.3 B expected in 2024. This growth is fueled by the ever-increasing demand for skilled professionals. The Bureau of Labor Statistics predicts an 11% increase in business analyst positions from 2020 to 2030, highlighting the significance of BI roles. Gain high-demand skills in Data Transformation, Charts and Page Layout, Advanced Analytics with Power BI, Building Reports & Special Visualizations, Building Narratives with Fabric and Working with Copilot Feature. Skill-based training modules address: 1) Introduction to Power BI, 2) Power BI Desktop and Data Transformation, 3) Data Analysis Expression (DAX), 4) Data Visualization, 5) Power BI Service, 6) Connectivity Modes, 7) Power BI Report Server, 8) Integrating R & Python in Power BI, 9) Advanced Analytics in Power BI, and 10) Hands-on Project

Enroll today (teams & executives are welcome): https://tinyurl.com/yc55jywf 

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 

Building Data Pipelines with Kafka and Nifi

Colleagues, in the “Building Data Pipelines with Kafka and Nifi” program you will learn about Kafka topics, brokers, and partitions, how to implement basic Kafka usage modes, use Kafka producers and consumers with Python, utilize the KafkaEsque graphical user interface, understand the core concepts of NiFi, NiFi flow and web UI components, understand direct data movement with HDFS, u se HBase with Python Happybase and Sqoop for database movement. Skill-based training modules include: 1) Working with the Kafka Message Broker - describes the producer-consumer model that enables input data to be reliably decoupled from output requests. Kafka producers and consumers are developed using Python, and internal broker operations are displayed using the Kafkaesque graphical user interface, 2) Working with NiFi Dataflow - writing pipeline data to the local file system, then to the Hadoop Distributed File System, and finally to Hadoop Hive tables. The entire flow process is constructed using the NiFi web Graphical User Interface. The creation of portable flow templates for all examples is also presented, 3) Big Data Movement and Storage - methods for moving data to and from the Hadoop Distributed File System. Hands-on examples include direct web downloads and using Python Pydoop to move data. Basic data movement between Apache HBase, Hive, and Spark using Python Happybase and Hive-SQL is also presented. Finally, movement of relational data to and from the Hadoop Distributed File System is demonstrated using Apache Sqoop.

Enroll today (teams & executives are welcome): https://tinyurl.com/a6bu77db 

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)

Data Science A-Z: Hands-On Exercises & ChatGPT Prize

DS colleagues, in the “Data Science A-Z: Hands-On Exercises & ChatGPT Prize” program you will learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization plus How to perform all steps in a complex Data Science project, create Basic Tableau Visualisations, perform Data Mining in Tableau, understand how to apply the Chi-Squared statistical test, apply Ordinary Least Squares method to Create Linear Regressions, assess R-Squared for all types of models, analyze the Adjusted R-Squared for all types of models, create a Simple Linear Regression (SLR), a Multiple Linear Regression (MLR) and much more. 28 sections • 217 lectures • 21h 13m total length. Skill-based training lessons include: 1) What is Data Science?, 2) Visualisation - Introduction to Tableau, How to use Tableau for Data Mining, Advanced Data Mining With Tableau, 3) Modeling - Stats Refresher, Simple Linear Regression, Multiple Linear Regression, 4) Logistic Regression, 5) Building a robust geodemographic segmentation model, 6) Assessing your model, Drawing insights from your model, Model maintenance, 7) Data Preparation - Business Intelligence (BI) Tools, ETL Phase 1: Data Wrangling before the Load, ETL Phase 2: Step-by-step guide to uploading data using SSIS, Handling errors during ETL (Phases 1 & 2), SQL Programming for Data Science, ETL Phase 3: Data Wrangling after the load, Handling errors during ETL (Phase 3), and 8) Communication - Working with people, Presenting for Data Scientists, and Homework Solutions. 

Enroll today (teams & executives are welcome): https://tinyurl.com/4eavzjh2 

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 

Introduction to Data Engineering (IBM)

Colleagues, the “Introduction to Data Engineering” program from IBM will jumpstart your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in the ecosystem. You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field. Next you will learn about the data engineering ecosystem, the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. You will become familiar with the components of a data platform and gain an understanding of several different types of data repositories such as Relational (RDBMS) and NoSQL databases, Data Warehouses, Data Marts, Data Lakes and Data Lakehouses. You’ll then learn about Big Data processing tools like Apache Hadoop and Spark. You will also become familiar with ETL, ELT, Data Pipelines and Data Integration. This course provides you with an understanding of a typical Data Engineering lifecycle which includes architecting data platforms, designing data stores, and gathering, importing, wrangling, querying, and analyzing data. You will also learn about security, governance, and compliance. You will learn about career opportunities in the field of Data Engineering and the different paths that you can take for getting skilled as a Data Engineer. You will hear from several experienced Data Engineers, sharing their insights and advice. By the end of this course, you will also have completed several hands-on labs and worked with a relational database, loaded data into the database, and performed some basic querying operations

Enroll today (teams & executives are welcome): imp.i384100.net/Jz3B9R 

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 

Data Engineering with AWS (Nanodegree Program)

Colleagues, in the “Data Engineering with AWS - Nanodegree Program” you will learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. Skill-based courses include: 1) Data Modeling - create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra, Introduction to Data Modeling - understand the purpose of data modeling, the strengths and weaknesses of relational databases, and create schemas and tables in Postgres, 3) NoSQL Data Models - when to use non-relational databases based on the data business needs, their strengths and weaknesses, and how to creates tables in Apache Cassandra (Project: Data Modeling with Apache Cassandra); 4) Cloud Data Warehouses - create cloud-based data warehouses. You’ll sharpen your data warehousing skills, deepen your understanding of data infrastructure, and be introduced to data engineering on the cloud using Amazon Web Services (AWS) - Introduction to Cloud Data Warehouses, Introduction to Data Warehouses, you'll be introduced to the business case for data warehouses as well as architecture, extracting, transforming, and loading data, data modeling, and data warehouse technologies, 5) ELT and Data Warehouse Technology in the Cloud - learn about ELT, the differences between ETL and ELT, and general cloud data warehouse technologies, 6) AWS Data Warehouse Technologies - to set up Amazon S3, IAM, VPC, EC2, and RDS. You'll build a Redshift data warehouse cluster and learn how to interact with it, 6) Implementing a Data Warehouse on AWS - implement a data warehouse on AWS (Project: Data Warehouse. You will build an ETL pipeline that extracts data from S3, stages data in Redshift, and transforms data into a set of dimensional tables for an analytics team); 7) Spark and Data Lakes - learn about the big data ecosystem and how to use Spark to work with massive datasets. You’ll also learn about how to store big data in a data lake and query it with Spark. Introduction to Spark and Data Lakes - learn how Spark evaluates code and uses distributed computing to process and transform data. You'll work in the big data ecosystem to build data lakes and data lake houses, 8) Big Data Ecosystem, Data Lakes, and Spark - learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it, 9) Spark Essentials - use Spark for wrangling, filtering, and transforming distributed data with PySpark and Spark SQL - Using Spark in AWS, learn to use Spark and work with data lakes with Amazon Web Services using S3, AWS Glue, and AWS Glue Studio, 10) Ingesting and Organizing Data in a Lakehouse. In this lesson you'll work with Lakehouse zones. You will build and configure these zones in AWS (Project: STEDI Human Balance Analytics - work with sensor data that trains a machine learning model. You'll load S3 JSON data from a data lake into Athena tables using Spark and AWS Glue, 11) Automate Data Pipelines. In this course, you'll build pipelines leveraging Airflow DAGs to organize your tasks along with AWS resources such as S3 and Redshift, 12) Automating Data Pipelines - build data pipelines, 13) Data Pipelines. In this lesson, you'll learn about the components of a data pipeline including Directed Acyclic Graphs (DAGs). You'll practice creating data pipelines with DAGs and Apache Airflow, 14) 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, 15) 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, 16) Production Data Pipelines - build Pipelines with maintainability, reusability and monitoring,  in mind. They will also learn about pipeline monitoring (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://tinyurl.com/4pxyw9vt

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 

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