The United Kingdom has more than earned its sterling reputation as a powerhouse of technological excellence. It is the go-to location for expert knowledge, inventive application, and faultless execution. Whether it's artificial intelligence, blockchain, cyber security, or data analytics, the UK is at the forefront of some of the world's most intriguing technological breakthroughs. Best-in-class tech firms require the best-in-class tech personnel. The UK workforce has a multitude of talents, whether it's access to professionals in AI, IoT, or cyber security: there are 240,000 digital technology employees in London alone.
Data analysing, irrespective of its form, can be extremely chaotic and challenging. This is where feature engineering steps in. A method to ease data analysis, feature engineering simplifies data reading for machine learning models. A feature or variable is nothing but the numerical representation of all kinds of data– structured and unstructured. Feature engineering is a vital part of the process of predictive modelling.
And learn to use it with one of the most popular way! One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Superset! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Apache Superset to solve their big data problems! What is this course about? This course covers all the fundamentals about Apache Spark Machine Learning Project with Scala and teaches you everything you need to know about developing Spark Machine Learning applications using Scala, the Machine Learning Library API for Spark.
Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets.
Databricks is founded by the creators of Apache Spark, Databricks combines the best of data warehouses and data lakes into a lakehouse architecture. Databricks is an American enterprise software company founded by the creators of Apache Spark. The company has also created Delta Lake, MLflow and Koalas, open source projects that span data engineering, data science and machine learning. Databricks develops a web-based platform for working with Spark, that provides automated cluster management and IPython-style notebooks. Gartner has classified Databricks as a leader in the last quadrant for Data Science and Machine Learning platforms.
Welcome to the most comprehensive Data Analytics course available on Udemy! When you become a Data Analyst, there are two things that you should be skilled at in order to be a master data analyst, Python and Tableau! This course is a great choice for beginners looking to expand their skills in Data Analytics. You also create a solid portfolio of your work online and can link it on your resume. At 11 hours, this Python and Tableau course will teach you the core principles of Data Analytics at every stage in the pipeline.
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done.
Welcome to the Python for Data Science - NumPy, Pandas & Scikit-Learn course, where you can test your Python programming skills in data science, specifically in NumPy, Pandas and Scikit-Learn. This course is designed for people who have basic knowledge in Python, NumPy, Pandas and Scikit-Learn packages. It consists of 330 exercises with solutions. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.
Last month, I wrote an article on building a data science learning roadmap with free courses offered by MIT. However, the focus of most courses I listed was highly theoretical, and there was a lot of emphasis on learning the math and statistics behind machine learning algorithms. While the MIT roadmap will help you understand the principles behind predictive modelling, what's lacking is the ability to actually implement the concepts learnt and execute a real-world data science project. After spending some time scouring the Internet, I found a couple of freely available courses by Harvard that covered the entire data science workflow?--?from programming to data analysis, statistics, and machine learning. Once you complete all the courses in this learning path, you are also given a capstone project that allows you to put everything you learnt in practice.