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.
Introduction to Analytics: Offers a foundational understanding of what analytics entails, including basic concepts and the significance of data analysis in decision-making.
Data Collection Methods: Covers various data collection techniques, including qualitative and quantitative methods, and the importance of ethical data collection.
Data Cleaning and Preparation: Discusses the steps for preparing data for analysis, which involves cleaning data, handling missing values, and ensuring data quality.
Statistical Analysis Basics: Introduces fundamental statistical concepts and techniques used to derive insights from data, including descriptive and inferential statistics.
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.
Using Excel for Analytics: Demonstrates how to use Excel, a common tool for data analysis, including functions, formulas, and pivot tables for analyzing data.
Introduction to SQL for Data Analysis: Provides basics on using SQL (Structured Query Language) to retrieve, filter, and manipulate data from databases.
Basics of Predictive Analytics: Offers an insight into predictive analytics, including simple forecasting models, to anticipate future trends based on historical data.
Introduction to Data Analytics Tools: Gives an overview of various analytics tools beyond Excel and SQL, possibly including Python, R, or specific analytics software.
Practical Applications of Analytics: Presents case studies or real-world examples illustrating how analytics can solve business problems, improve decision-making, and drive strategy.
Completing this Analytics Course certifies your proficiency in data analysis, statistical methods, and the use of key analytics tools. This certification demonstrates your ability to transform raw data into actionable insights, enhancing decision-making processes in various business contexts. It signifies your understanding of data visualization, predictive modeling, and data-driven strategy development, equipping you with the skills necessary to excel in analytics. This credential is a testament to your dedication to continuous learning and your commitment to leveraging data for strategic advantage.
Data-driven Decision Making: By teaching you how to analyse data effectively, our analytics course helps you make well-informed decisions and develop strategic plans.
Career Possibilities: Gaining expertise in analytics can lead to a variety of employment options in disciplines like financial analysis, business intelligence, data science, and marketing analytics.
Desired Proficiencies: Acquire expertise in data analysis tools and methodologies, which will increase your marketability to employers in the data-driven economy of today.
Learning: how to extract useful information from data to spot patterns, streamline procedures, and forecast outcomes that will propel your company's expansion and success.
Competitive advantage: Gain a competitive advantage in the labor market by becoming an expert in analytics, a skill that is becoming more and more in demand across a range of sectors and businesses.
Problem-solving: Developing good problem-solving abilities involves analysing complex datasets and coming up with data-driven insights-based answers to business problems.
Versatility: Because analytics abilities are transferable to other domains, you can explore job prospects in a wide range of industries, including technology, healthcare, finance, and retail.
Career: Enrol in an area that is always changing to take advantage of the many chances for continuing education and career advancement.
Skills: The development of quantitative skills includes using modeling approaches, statistical procedures, and algorithms to extract useful information from data.
Investing in skills: necessary for the workplace of the future is a good way to future-proof your business since more and more companies are depending on data analysis to spur innovation, productivity, and competitiveness.
- Overview of data analysis and its importance in business
- Types of analytics: Descriptive, Predictive, Prescriptive
- Role of data in decision-making processes
- Introduction to common tools: Tableau, PowerBI, Excel
- Ethical considerations in data collection and analysis
- Data sources: Primary and secondary data
- Data collection methods (surveys, web scraping, databases)
- Data cleaning techniques (handling missing values, outliers)
- Data transformation and feature engineering
- Data storage concepts (structured vs. unstructured data)
- Descriptive statistics: Mean, median, mode
- Data visualization basics (histograms, scatter plots)
- Identifying data patterns and trends
- Outlier detection and handling methods
- Correlation and causation analysis
- Inferential statistics and probability theory
- Hypothesis testing (t-tests, chi-square tests, ANOVA)
- Measures of central tendency and variability
- Confidence intervals and margin of error
- Regression analysis: Linear and logistic regression
- Principles of effective data visualization
- Types of charts and their uses (bar, line, pie, heatmaps)
- Designing dashboards for different audiences
- Interactive visualization techniques
- Data storytelling for impactful presentations
- Time series analysis and forecasting methods
- Clustering and segmentation analysis
- Decision trees and classification techniques
- Introduction to machine learning in business analytics
- Model evaluation and selection
- Basics of SQL for data manipulation
- Creating databases and relationships
- Aggregating data with SQL (GROUP BY, JOIN)
- Data modeling for business intelligence (star and snowflake schemas)
- Case study: Building a business model with SQL
- Connecting and preparing data in Tableau
- Creating basic visualizations (charts, maps)
- Advanced Tableau functions (LOD calculations, table calculations)
- Building interactive dashboards and stories
- Publishing and sharing visualizations on Tableau Server/Online
- Introduction to PowerBI workspace and components
- Data import and transformation with Power Query
- Data modeling and relationships in PowerBI
- Creating and customizing visualizations
- Publishing and collaborating on PowerBI Service
- Selecting a real-world dataset for analysis
- Defining business questions and objectives
- Conducting data analysis and visualization
- Presenting findings in a comprehensive dashboard
- Peer review and feedback on project
