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via Udemy |
Go to Course: https://www.udemy.com/course/applied-statistics-and-data-preparation-with-python/
If you're interested in mastering data analysis and data science, the Coursera course on Python Programming for Applied Statistics is an excellent choice. This course provides a fast-track pathway to understanding how to leverage Python for data-driven decision-making, an essential skill in today's data-centric world. **Why Take This Course?** According to industry leader SAS, there are five compelling reasons to learn data analysis and data science: 1. **Problem-Solving Skills:** Enhance your ability to think analytically and approach complex problems with confidence. 2. **High Demand:** Data Analysts and Data Scientists are highly sought after, with demand projected to grow as more organizations recognize the value of data. 3. **Ubiquity of Data:** Data is everywhere—from business operations to IoT devices—and knowing how to extract insights is invaluable. 4. **Ever-Growing Importance:** As data continues to grow exponentially, so does the significance of data analysis in driving strategic decisions. 5. **Diverse Skill Set:** The field combines elements from computer science, business, mathematics, and communication, making it an intellectually enriching career path. **Course Content & Structure** The course is structured to guide you from fundamental Python programming to advanced applied statistics techniques, tailored specifically for data analysis. It begins with foundational Python skills, including creating calculators and basic programming concepts, then moves into data understanding and preparation through real-world datasets. Notable modules include: - Data Mining Process & CRISP-DM methodology - Data Loading and Data Handling techniques (reading datasets, selecting variables and rows) - Statistical methods such as descriptive statistics, correlation, hypothesis testing (T-tests, Chi-Square, ANOVA), and regression analysis - Data processing tasks like sorting, renaming, handling missing values, and removing duplicates - Visualizations using Python to uncover insights more effectively **Review & Recommendations** This course is highly practical and well-suited for beginners with some Python knowledge, especially those looking to deepen their understanding of applied statistics in the context of data science. Its focus on real datasets and step-by-step processes makes it accessible and engaging. I highly recommend this course if you want to build a solid foundation in data analysis, excel in using Python for statistical tasks, and prepare for advanced topics like machine learning. Additionally, upon successful completion and passing an exam at EMHAcademy, you can earn the SVBook Certified Data Miner using Python certification, which adds value to your professional profile. **In Summary:** - Perfect for aspiring data analysts and data scientists - Practical, hands-on approach with real datasets - Covers essential statistical and data processing techniques - Prepares you for advanced data science topics and certifications Embark on this learning journey to unlock new career opportunities and become proficient in turning data into actionable insights!
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 for Applied Statistics. In CRISP-DM data mining process, Applied Statistics is at the Data Understanding stage. This course also covers Data processing, which is at the Data Preparation Stage. You will need to know some Python programming, and you can learn Python programming from my "Create Your Calculator: Learn Python Programming Basics Fast" course. You will learn Python Programming for applied statistics. You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using Python certificate: - Create Your Calculator: Learn Python Programming Basics Fast (R 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, in the future)- Machine Learning with Python (Modeling and Evaluation)ContentGetting StartedGetting Started 2Getting Started 3Data Mining ProcessDownload Data setRead Data setModeMedianMeanRangeRange One ColumnQuantileVarianceStandard DeviationHistogramQQPLotShapiro TestSkewness and KurtosisDescribe()CorrelationCovarianceOne Sample T TestTwo Sample TTestChi-Square TestOne Way ANOVASimple Linear RegressionMultiple Linear RegressionData Processing: DF.head()Data Processing: DF.tail()Data Processing: DF.describe()Data Processing: Select VariablesData Processing: Select RowsData Processing: Select Variables and RowsData Processing: Remove VariablesData Processing: Append RowsData Processing: Sort VariablesData Processing: Rename VariablesData Processing: GroupBYData Processing: Remove Missing ValuesData Processing: Is THere Missing ValuesData Processing: Replace Missing ValuesData Processing: Remove Duplicates