Data Analyst - SimpliLearn Subscription

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

The Data Analyst Track allow you to become an expert in Data Analytics. During this Learning Track you will follow 13 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 Analytics.

Introduction to Data Analytics

Simplilearn’s Introduction to Data Analytics course will give you insights into applying data and analytics principles in your business. You will gain an understanding of the complete data analytics lifecycle, from problem definition to solution deployment. Through various industry-specific examples and case studies, you will learn how analytics, data visualization, and data science methodologies can be used to drive better business decisions.

Key Learning Objectives

• Understand how to solve analytical problems in real-world scenarios
• Define effective objectives for analytics projects
• Work with different types of data
• Understand the importance of data visualization to help make more effective business decisions
• Understand charts, graphs, and tools used for analytics and visualization and use them to derive meaningful insights
• Create an analytics adoption framework
• Identify upcoming trends in the data analytics field

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, and distribution. Additionally, 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 Learn 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

SQL for Data Analytics

This course gives you the information you need to successfully start working with SQL databases and make use of the database in your applications. Learn how to correctly structure your database, author efficient SQL statements and clauses, and manage your SQL database for scalable growth

Key Learning Objectives

• Understand databases and relationships
• Use common query tools and work with SQL commands
• Understand transactions, creating tables and views
• Comprehend and execute stored procedures

Business Analytics with Excel

Business Analytics with Excel training will boost your analytics career with powerful new Microsoft Excel skills. This business analytics training course will equip you with the concepts and hard skills required for a strong analytics career. You’ll learn the basic concepts of data analysis and statistics, helping promote data-driven decision making. Your new knowledge of this commonly used tool combined with official business analytics certification is guaranteed to ensure career success.

Key Learning Objectives

• Understand the meaning of business analytics and its importance in
  the industry
• Grasp the fundamentals of Excel analytics functions and conditional
  formatting
• Learn how to analyze with complex datasets using pivot tables and
  slicers
• Solve stochastic and deterministic analytical problems using tools like
  scenario manager, solver, and goal seek
• Apply statistical tools and concepts like moving average, hypothesis
  testing, ANOVA, and regression to data sets using Excel
• Represent your findings using charts and dashboards
• Get introduced to the latest Microsoft analytic and visualization tools,
  such as Power BI

Tableau Training

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 the 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,
  treemap, 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

Power BI Training

Microsoft Power BI is a suite of tools used to analyze your data and extract business insights by building interactive dashboards. This Power BI Training course will help you get the most out of Power BI, enabling you to solve business problems and improve operations.

This Power BI Training course will help you master the development of dashboards from published reports, discover greater insight from your data with Quick Insights, and learn practical recipes for the various tasks that you can do with Microsoft Power BI—from gathering your data to analyzing it. This course also contains some handy recipes for troubleshooting various issues in Power BI.

Key Learning Objectives

• Create dashboards from published reports
• Quickly generate visuals and dashboards with Quick Insights
• Use natural language in the Q&A feature to quickly generate visuals
  for actionable insight
• Create and manage data alerts
• Get report layout and data visualization best practices
• Understand which charts/graphs to use depending on the question
  being answered or the story being told
• Use shapes to design, emphasize, and tell a story
• See how to incorporate custom visuals into your reports and
  dashboards
• Share reports and dashboards, as well as the pros and cons of each
• Complete a Power BI data analysis/visual project from start to finish
• Improve team collaboration with Microsoft Teams
• Know how to get and prepare your data for analysis and visualization
• Learn how to create relationships between tables in your data model
• Create calculated columns and measures using the DAX languag

Python for Data Science

Kickstart your learning of Python for data science with this introductory course and familiarize yourself with programming. Upon completion of this course, carefully crafted by IBM, 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 the concepts of
  variables, strings, functions, loops, and conditions
• Understand the nuances of lists, sets, dictionaries, conditions and
  branching, objects, and classes
• Work with data in Python, including reading and writing files, loading,
  working, and saving data with Pandas

Data Visualization with Python

Data visualization plays an essential role in the representation of both small and large-scale data. In this Data Visualization with Python course, you will learn how to create impressive graphics and charts and customize them to make them more productive and more pleasing to your audience. You will gain expertise in several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium to extract information, better understand the data, and make more effective decisions.

Key Learning Objectives

• Learn data visualization and best practices when creating plots and
  visuals
• Master basic plotting with Matplotlib
• Generate different visualization tools using Matplotlib such as line
  plots, area plots, histograms, bar charts, box plots, and pie charts
• Understand Seaborn, a data visualization library in Python, and how to
  use it to create attractive statistical graphics
• Understand Folium and how to use it to create maps and visualize
  geospatial data

Programming Basics and Data Analytics with Python

Learn how to analyze data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in Pandas, use the SciPy library of mathematical routines, and perform machine learning using scikit-learn. This course will take you from the basics of Python to the many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.

Key Learning Objectives

• Import data sets
• Clean and prepare data for analysis
• Manipulate Pandas DataFrame
• Summarize data
• Build machine learning models using scikit-learn
• Build data pipelines

R Programming for Data Science

Gain 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, strings, vectors, factors, and vector
  operations
• Gain a fundamental knowledge of arrays and matrices, lists, and data
  frames
• Get an understanding of 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 learn how to work on strings and dates in R

Data Visualization with R

In this Data Visualization with R course by IBM, you will learn how to create beautiful graphics and charts, customizing their look and feel using the open-source language R. This course will help you learn how to leverage a software tool to visualize data and enable you to extract information, better understand the data, and make more effective decisions.

Key Learning Objectives

• Learn how to create beautiful graphics and charts
• Understand how to customize the look and feel of them
• Master the creation of maps in R
• Gain expertise in the creation of scatter plots, line plots, regression,
  bar charts, histograms, pie charts, word clouds, radar charts, waffle
  charts, and box plots

Data Science with R

The next step to mastering data science is learning R—the most indemand open source technology in the field. 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 a 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 learn how to use linear and 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

Data Analyst Capstone

This Data Analyst 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. This project is the final step in the learning path and will enable you to showcase your expertise in data analytics to future employers.