Description
Why learn Data Analysis and Data Science?According to SAS, the five reasons are1. Gain problem solving skillsThe ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life. 2. High demandData Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase. 3. Analytics is everywhereData is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.4. It's only becoming more importantWith the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities. 5. A range of related skillsThe great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise. The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities. This is the bite-size course to learn Python Programming. You will learn Python Programming very fast and You will create your own calculator very soon after learning the course. You can look into the following courses to get SVBook Certified Data Miner using PythonSVBook Certified Data Miner using Python are given to people who have completed the following courses:- Create Your Calculator: Learn Python Programming Basics Fast (Python Basics)- Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)- Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation)- Machine Learning with Python (Modeling and Evaluation)and passed a 50 questions Exam. The four courses are created to help learners understand about Python programming basics, then applied statistics (descriptive, inferential, regression analysis) and data visualizations (bar chart, pie chart, boxplot, scatterplot matrix, advanced visualizations with seaborn, and Plotly interactive charts ) with data processing basics to understand more about the data understanding and data preparation stage of IBM CRISP-DM model. The learner will then learn about machine learning and confusion matrix, which is the modeling and evaluation stages of the IBM CRISP-DM model. The learner will be able to do data mining projects after learning the courses. ContentGetting StartedHello World SoftwareVariables and Data TypesData types ConversionArithmetic OperatorsComparison OperatorsAssignment OperatorsBoolean OperatorsDecision Making I (IF statements)Loop (while loop, for loop)ListsFunctionsModulesObject and ClassesCreate Your own Calculator
