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          Opleidingen - Data Scientist - SimpliLearn Subscription bij SimpliLearn

          Data Scientist - SimpliLearn Subscription



          • InstituutSimpliLearn
          • SoortE-Learning
          • Totale lesduur1 dagen
          • Kosten€1.500 excl. BTW

          Gratis brochure

          🗂 10 online trainingen | 🇬🇧 Taal: Engels | 🗓 Abonnement: year | 🎯 Vakgebieden: IT,  data, marketing

          The Data Scientist Track allow you to become an expert in Data . During this Learning Track you will follow 10 different trainings to develop your knowledges and skills in the field.
          For each training completed, receive a certification and continue your progress to become expert in Data.

          Data Science in Real life

          Data Science is the highly sought field of the century. Explore the truth about what Data Science is and hear from real practitioners telling real stories about what it means to work in Data Science and use cases for the same.

          Key Learning Objectives

          • Gain fundamental knowledge of what is Data Science and what do Data Science people do
          • Learn about Data Science in a business context and what is the future of Data Science
          • Understand Data Science applications and discover some use cases for Data Science

          Statistics Essentials

          Statistics is the science of assigning a probability to an event based on experiments. It is the application of quantitative principles to the collection, analysis, and presentation of numerical data. Ace the fundamentals of Data Science, statistics, and Machine Learning with this course. It will enable you to define statistics and essential terms related to it, explain measures of central tendency and dispersion, and comprehend skewness, correlation, regression, distribution. You will be able to make data-driven predictions through statistical inference.

          Key Learning Objectives

          • Understand the fundamentals of statistics
          • Work with different types of data
          • How to plot different types of data
          • Calculate the measures of central tendency, asymmetry, and variability
          • Calculate correlation and covariance
          • Distinguish and work with different types of distribution
          • Estimate confidence intervals Perform hypothesis testing
          • Make data-driven decisions
          • Understand the mechanics of regression analysis
          • Carry out regression analysis Use and understand dummy variables
          • Understand the concepts needed for data science even with Python and R!

          R Programming for Data Science

          IGain insight into the R Programming language with this introductory course. An essential programming language for data analysis, R Programming is a fundamental key to becoming a successful Data Science professional. In this course you will learn how to write R code, learn about R’s data structures, and create your own functions. After the completion of this course, you will be fully able to begin your first data analysis.

          Key Learning Objectives

          • Learn about math, variables, and strings, vectors, factors, and vector operations
          • Gain fundamental knowledge on arrays and matrices, lists, and data frames
          • Get understanding on conditions and loops, functions in R, objects, classes, and debugging
          • Learn how to accurately read text, CSV and Excel files plus how to write and save data objects in R to a file
          • Understand and work on strings and dates in R

          Data Science with R

          The next step to a data scientist is learning R - the upcoming and most in-demand open source technology. R is an extremely powerful Data Science and analytics language which has a steep learning curve and a very vibrant community. This is why it is quickly becoming the technology of choice for organizations who are adopting the power of analytics for competitive advantage.

          Key Learning Objectives

          • Gain a foundational understanding of business analytics
          • Install R, R-studio, and workspace setup, and learn about the various R packages.
          • Master R programming and understand how various statements are executed in R. Gain an in-depth understanding of data structure used in R and learn to import/export data in R.
          • Define, understand and use the various apply functions and DPYR functions.
          • Understand and use the various graphics in R for data visualization.
          • Gain a basic understanding of various statistical concepts.
          • Understand and use hypothesis testing method to drive business decisions.
          • Understand and use linear, non-linear regression models, and classification techniques for data analysis.
          • Learn and use the various association rules and Apriori algorithm.
          • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering.

          Python for Data Science

          Kickstart your learning of Python for Data Science with this introductory course and familiarize yourself with programming. Carefully crafted by IBM, upon completion of this course you will be able to write your Python scripts, perform fundamental hands-on data analysis using the Jupyterbased lab environment, and create your own Data Science projects using IBM Watson.

          Key Learning Objectives

          • Write your first Python program by implementing concepts of variables, strings, functions, loops, conditions
          • Understand the nuances of lists, sets, dictionaries, conditions and branching, objects and classes
          • Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas

          Data Science with Python

          This Data Science with Python course will establish your mastery of Data Science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing. Python is a required skill for many Data Science positions, so jump start your career with this interactive, hands-on course.

          Key Learning Objectives

          • Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics
          • Install the required Python environment and other auxiliary tools and libraries
          • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
          • Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions
          • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
          • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
          • Gain expertise in Machine Learning using the Scikit-Learn package
          • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline S T E P 1 2 3 4 5 6 7 8 9 10 17 |
          • Use the Scikit-Learn package for natural language processing
          • Use the matplotlib library of Python for data visualization
          • Extract useful data from websites by performing web scraping using Python
          • Integrate Python with Hadoop, Spark, and MapReduce

          Machine Learning

          Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

          Key Learning Objectives

          • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
          • Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
          • Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning
          • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
          • Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
          • Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

          Tableau Desktop 10

          This Tableau Desktop 10 training will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. It is an essential asset to those wishing to succeed in Data Science.

          Key Learning Objectives

          • Grasp the concepts of Tableau Desktop 10, become proficient with statistics and build interactive dashboards
          • Master data sources and datable blending, create data extracts and organize and format data
          • Master arithmetic, logical, table and LOD calculations and ad-hoc analytics
          • Become an expert on visualization techniques such as heat map, tree map, waterfall, Pareto, Gantt chart and market basket analysis
          • Learn to analyze data using Tableau Desktop as well as clustering and forecasting techniques
          • Gain command of mapping concepts such as custom geocoding and radial selections
          • Master Special Field Types and Tableau Generated Fields and the process of creating and using parameters
          • Learn how to build interactive dashboards, story interfaces and how to share your work

          Big Data Hadoop and Spark Developer

          Learn how to work Big Data and its components. Deep-dive into Hadoop and its ecosystem including MapReduce, HDFS, Yarn, HBase, Impala, Sqoop and Flume. This course provides an introduction to Apache Spark which is a next step after Hadoop. After completing this course, you will be able to successfully pass the Cloudera CCA175 certification but embrace this technology as part of your role as a Data Scientist.

          Key Learning Objectives

          • Master the concepts of the Hadoop framework and its deployment in a cluster environment
          • Understand how the Hadoop ecosystem fits in with the data processing lifecycle
          • Learn to write complex MapReduce programs
          • Describe how to ingest data using Sqoop and Flume Get introduced to Apache Spark and its components
          • List the best practices for data storage
          • Explain how to model structured data as tables with Impala and Hive

          Data Science Capstone Project

          This Data Science Capstone project will give you an opportunity to implement the skills you learned throughout this Program. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers.

          Key Learning Objectives

          Simplilearn’s online Data Science Capstone course will bring you through the Data Science decision cycle, including data processing, building a model and representing results. The project milestones are as follows:

          • Data Processing - In this step, you will apply various data processing techniques to make raw data meaningful.
          • Model Building - You will leverage techniques such as regression and decision trees to build Machine Learning models that enable accurate and intelligent predictions. You may explore Python, R or SAS to build your model. You will follow the complete model-building exercise from data split to test and training and validating data using the k-fold cross-validation process.
          • Model Fine-tuning - You will apply various techniques to improve the accuracy of your model and select the champion model that provides the best accuracy.
          • Dashboarding and Representing Results - As the last step, you will be required to export y

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