The analytics course offered by SkillFloor provides comprehensive training in data analysis and business analytics. Geared towards aspiring data analysts and business analysts, the curriculum covers a wide range of topics essential for success in these roles. Students will delve into techniques for data collection, cleaning, and analysis, as well as learn how to interpret and visualize data effectively. The course also includes modules on statistical analysis, predictive modelling, and data-driven decision-making, equipping learners with the skills needed to derive valuable insights from complex datasets. Whether pursuing a career as a data analyst or a business analyst, this course offers a solid foundation in data analytics that is essential for success in today's data-driven world.

With a focus on practical skills and real-world applications, the data analyst and business analyst courses provided by SkillFloor offer a dynamic learning experience. Participants will gain hands-on experience using popular analytics tools and software, honing their ability to manipulate data and extract meaningful insights. Through case studies and projects, students will have the opportunity to apply their knowledge to solve business challenges and make data-driven recommendations. Whether individuals are looking to transition into a career in data analytics or enhance their existing skill set, these courses provide a comprehensive understanding of data analysis and business analytics concepts, empowering learners to excel in a competitive job market.

Course Highlights

1. Introduction to Analytics: Offers a foundational understanding of what analytics entails, including basic concepts and the significance of data analysis in decision-making.

2. Data Collection Methods: Covers various techniques for collecting data, including both qualitative and quantitative methods, and the importance of ethical data collection.

3. Data Cleaning and Preparation: Discusses the steps for preparing data for analysis, which involves cleaning data, handling missing values, and ensuring data quality.

4. Statistical Analysis Basics: Introduces fundamental statistical concepts and techniques used to derive insights from data, including descriptive and inferential statistics.

5. Introduction to Data Visualization: Explores the principles of effectively presenting data through visual means, such as charts and graphs, to facilitate understanding and decision-making.

6. Using Excel for Analytics: Demonstrates how to use Excel, a common tool for data analysis, including functions, formulas, and pivot tables for analyzing data.

7. Introduction to SQL for Data Analysis: Provides basics on using SQL (Structured Query Language) to retrieve, filter, and manipulate data from databases.

8. Basics of Predictive Analytics: Offers an insight into predictive analytics, including simple forecasting models, to anticipate future trends based on historical data.

9. Introduction to Data Analytics Tools: Gives an overview of various analytics tools beyond Excel and SQL, possibly including Python, R, or specific analytics software.

10. Practical Applications of Analytics: Presents case studies or real-world examples illustrating how analytics can solve business problems, improve decision-making, and drive strategy.




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