Financial Data Analytics Certification Course

Why Select this Training Course?

Financial data analytics is performed to better understand a company’s financial health and take appropriate measures to boost performance. Financial data analytics helps firms obtain fresh insights, anticipate future performances, and build effective solutions to cope with performance difficulties. Experts in financial data are needed to develop dashboards and visualization tools for analyzing and presenting financial data to decision-makers. Understanding the rudiments of financial data is a vital skill in the financial industry.

How is data analytics employed in the finance industry?

Businesses across the globe are utilizing data analytics to boost internal operations. They rely on data analytics to better comprehend customers’ needs, allowing stakeholders to make decisions that enhance their organization’s performance. Data analytics helps finance departments analyze key indicators and spot revenue turnover fraud. It also turns structured and unstructured data into useful insights for improved decision-making. Finally, data analytics helps comprehend KPIs.

What makes data analytics different from data analysis?

Data analytics are tools employed in evaluating raw data that help businesses make informed decisions regarding performance and strategies. The processes include extracting valuable information from vast amounts of data and interpreting such data using logical and analytical reasoning to establish trends, patterns, or relationships. There are different processes and tools employed in data analytics, most of which are automated using algorithms. These algorithms, in turn, help to reveal specific metrics and trends in mass data that would have otherwise been lost. On the other hand, data analysis involves cleaning, transforming, and modeling data so that companies and businesses can use them.

The Rcademy Financial Data Analytics Certification Course is designed to provide participants with the fundamentals of data science tools and how they are employed in the finance industry. Participants will learn how to utilize statistical methods in evaluating financial data, designing strong financial data modeling, and evaluating the capabilities of programming languages. The course covers time series data, estimates, and the development of forecasts for financial purposes. Attendees will also learn how to apply financial analytics in real-world scenarios, algorithmic trading techniques, and numerical schemes in machine-learned used in performing statistical evaluation on financial data.

Who Should Attend?

The Financial Data Analytics Certification Course by Rcademy is suitable for:

• Data analysts: tasked with collecting, evaluating, and storing data from market research, logistics, sales numbers, and linguistics
• Finance Controllers: responsible for drafting the company’s financial report
• Accountants: tasked with accounting functions such as account analysis, auditing, or financial statement analysis
• Data scientists: responsible for identifying issues present in businesses and using data to design solutions for effective decision-making
• Finance analysts: charged with monitoring a firm’s financial operations, evaluating business performances and market situations to generate forecasts
• Data engineers: responsible for developing systems that gather, manage and transform raw data into valuable information for finance experts and business analysts
• Business analysts: tasked with carrying out market analyses, evaluating product lines, and the general profitability of a business
• Financial Auditors: entrusted with analyzing financial statements, papers, data, and accounting entries for the company
• Tax experts: tasked with collecting data and explaining the best tax options to customers, and designing periodic tax compliance reports
• Professionals interested in furthering their interest in data analytics and how it is employed in the finance sector

What are the Course Objectives?

The Financial Data Analytics Certification Course by Rcademy is designed to develop participants’ knowledge and understanding of financial data analytics. On completion of the course, candidates would have attained the following objectives:

• Understand how to describe the time series data
• Learn how to design an Autoregressive Integrated Moving Average (ARIMA) model
• Understand the fundamentals of data analytics and its relationship with the finance industry
• Learn basic trading algorithm
• Understand the forecasting processes and how it is employed in financial data analytics
• Learn how to utilize financial statements in evaluating the financial performance of a company
• Learn how data analytics are employed in driving critical decision-making in finance
• Understand the DCF or discounted cash flow methods and their usage in financial decision-making
• Gain practical insights into how Excel is applied in data science and analytics
• Develop models, interpret results, form hypotheses, and formulate practical recommendations
• Learn how to utilize Excel and Spreadsheet in performing data analysis and how to build a budget that works

How will this Course be Presented?

The Financial Data Analytics Certification Course is designed to meet attendees’ expectations while enhancing their knowledge and skills about the subject matter. Different practical techniques that will ensure participants’ constant and active learning will be utilized in delivering the course. Renowned professionals and experts in the field with several years of practice and experience will teach the course. The course modules are drafted from exhaustive and careful research into the subject matter.

The Rcademy Financial Data Analytics Certification Course integrates practical and theoretical learning presented through lecture notes, case studies, real-life scenarios, and slides on the concepts. Attendees will also partake in seminar workshops, presentations, quizzes, and constant feedback on lessons taught to gauge and determine their maximum satisfaction.

What are the Topics Covered in this Course?

Module 1: Introduction to Data Analytics

• Introduction and definition of terms
• Types of data analytics
• Importance of data analytics
• Data analytics tools
• Steps in data analytics
• Use cases
• Differences between data analytics vs. analysis
• Applications
• The data analytics process

Module 2: Fundamentals of Financial Analytics and Time-Series Data

• Overview of financial analytics
• Business forecasting and time-series data
• Applying financial analytics to real-world situations
• Forecasting performance measurement metrics and distances
• Time series data evaluation techniques
• Modern portfolio theory
• Basics of algorithmic trading

Module 3: Evaluation of Data

• Importance of metrics
• Industry data uses and sources
• Annual reports and footnotes
• Introduction to RONA, EVA, EBITDA
• Benchmarking for evaluation purposes
• Measuring the results of the analysis
• Short-term success evaluation measures and process

Module 4: The ARIMA Model and Stationarity

• Fundamentals of stationarity
• Identifying time series data using stationarity
• Introduction to the ARIMA model
• Components of the ARIMA model
• Designing an ARIMA forecasting model through R
• Differencing in stationarity

Module 5: Holt-Winters Model and Performance Measures

• Exponential smoothing techniques
• Forecasting performance measures
• Analyzing financial data using time series data
• Moving average
• The Holt-Winters method
• Linear regression
• Autoregression

Module 6: Excel Basics in Data Analytics

• Basics of data analysis through spreadsheet and Excel
• Spreadsheet basics
• Entering and editing data
• Copying and filing data
• Moving data
• Standard functions employed data analysts
• The basics of formulas

Module 7: Financial Analytics in Practice

• Introduction to financial analytics
• Return on equity
• Account receivable turnover
• Economic value added
• Inventory turnover
• Return on assets
• Accounts payable turnover
• EBIT and EBITDA
• Monetary cycle
• Operating leverage
• Breakeven point

Module 8: Financial Functions in Excel

• Date functions in Excel
• Using Discounted Cash Flows (DCF) in Excel
• Designing a loan schedule with PMT function
• Measuring the IRR or internal rate of return in Excel
• Using ROUND in financial models
• MATCH, INDEX, and their combinations

Module 9: Data Analytics in Finance

• Overview of data analytics in finance
• Importance of data analytics in finance
• The roles and responsibilities of data analysts in finance
• Industries utilizing data analytics
• Differences and similarities between data analysts and financial analysts

Module 10: Going Beyond the Basics in Financial Analysis

• Introduction to Du Pont analysis
• The two types of free cash flow (FCF)
• Improving return on equity (ROE)
• Altman’s Z-score
• Basics of sensitivity analysis
• Fundamentals of trend analysis
• Introduction to scenario analysis and how to measure it