Why Should I do this Financial Analytics Course?

To be an expert in the Financial Analytics and to grow in the organization with Additional skills.

I donâ€™t have a background in Finance, Can I Do this Financial Analytics Training Certification?

You can still do this Financial Analytics Course as it teaches you the basics of Finance and will enable you to Switch your Existing Career to the Finance Domain.

Would this Financial Analytics Certification Course help me in my career advancement?

For Existing Working professionals It will add another feather in the Cap and enhance the Skills and for Freshers, it will be a booster in their Early Stages to take up the Right Job in the Righ Company.

Can fresher or nontechnical background learners choose this Financial Analytics Course? Is this good for switching careers in different areas?

The Candidates from Non-Technical Background are welcome to this Financial Analytics Certification in order to have an understanding of the Financial angle of the Company as they already know the Technical ones. It will be a good option for the non-technical People since they are more prone to the Finance World.

Will I get a degree after Completing this Financial Analytics Training?

You will be issued a certificate stating that the Desired Candidate has successfully Completed this Financial Analytics Course with EduCBA.

Where to Enroll in this Financial Analytics Course?

Please visit the EDUCBA Website and fill-up the form displayed there online and pay the Fees.

Do you have any tie-ups with Corporations?

We do have good Relationship will corporate and we have placed many Students from our Batch who are top-performing in the Corporate.

FINANCIAL ANALYTICS in R
Specialization | 5 Course Series

This Financial Analytics Training includes 5 Course with 10+ hours of video tutorials and One year access. Through these tutorials, you shall learn financial and statistical formulas and their analysis with practical examples. We shall cover Time Value of Money, Loan Repayment, Investment Project Calculations, Bond Calculations, Stock price return calculations and a lot other such concepts.

What you'll get

10+ Hours
5 Courses
Course Completion Certificates
One year access
Self-paced Courses
Technical Support
Mobile App Access
Case Studies

Synopsis

Courses: You get access to all 5 course, Projects for the lifetime
Hours: 10+ Video Hours
Core Coverage: Through these tutorials, you shall learn financial and statistical formulas and its analysis with practical examples.
Course Validity: One year access
Eligibility: Anyone serious about learning Financial Analytics
Pre-Requisites: Basic knowledge about Finance concepts would be preferable
What do you get? Certificate of Completion for the 5course, Projects
Certification Type: Course Completion Certificates
Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced financial analytics skills
Type of Training Video Course – Self Paced Learning

Content

MODULE 1: Data Analytics for Finance: Tools and Techniques

Courses	No. of Hours	Certificates
Financial Analytics in R - Beginners	3h 38m	✔
Financial Analytics and Statistical Tools	1h 6m	✔
Financial Analytics with Python	2h 8m	✔

MODULE 2: Financial Analytics with R

Courses No. of Hours Certificates Details

Financial Analytics in R - Intermediate 1h 28m ✔
Financial Analytics in R - Advanced 1h 35m ✔

Courses	No. of Hours	Certificates	Details
Financial Analytics in R - Intermediate	1h 28m	✔
Financial Analytics in R - Advanced	1h 35m	✔

Description

Welcome to the "Financial Analytics Masterclass: Tools and Techniques" course! This comprehensive program is designed to equip you with the essential skills and knowledge needed to excel in the field of financial analytics. Whether you are a finance professional looking to enhance your analytical capabilities or an aspiring data scientist interested in the finance industry, this course will provide you with the foundational understanding and practical experience necessary to succeed.

Throughout this course, you will delve into a wide range of topics related to financial analytics, including data collection, cleaning, analysis, and interpretation. You will also learn how to leverage various tools and programming languages such as R and Python to perform sophisticated analyses and extract valuable insights from financial datasets. From basic statistical techniques to advanced machine learning algorithms, you will explore a diverse array of methodologies tailored specifically for financial applications.

Through a combination of theoretical lectures, hands-on exercises, and real-world case studies, you will have the opportunity to apply your newfound knowledge in practical scenarios, solidifying your understanding and honing your skills. By the end of the course, you will be equipped with the expertise and confidence to tackle complex financial analytics challenges and make data-driven decisions with precision and efficiency.

Whether you aspire to advance your career in finance, pursue further studies in data science, or simply broaden your skill set, this course will provide you with the tools and techniques needed to thrive in today's data-driven financial landscape. Get ready to embark on an exciting journey into the world of financial analytics and unlock new opportunities for professional growth and success!

Module 1: Data Analytics for Finance: Tools and Techniques

This module provides an overview of the tools and techniques essential for performing data analytics in the field of finance. Participants will learn how to collect, clean, and analyze financial data using various tools and software. Topics covered include data visualization, statistical analysis, regression modeling, and machine learning algorithms tailored for financial applications. Through hands-on exercises and real-world case studies, participants will gain practical experience in applying data analytics techniques to financial datasets, enabling them to extract valuable insights and make data-driven decisions.

Financial Analytics in R - Beginners (3h 53m): This module serves as an introduction to financial analytics using the R programming language, catering to beginners. Participants will learn the basics of R programming and how to apply it to financial data analysis, covering topics such as data manipulation, visualization, and basic statistical analysis.

Financial Analytics and Statistical Tools (1h 6m): This segment provides an overview of statistical tools commonly used in financial analytics. Participants will explore key statistical concepts and learn how to apply them to financial datasets, enabling them to make informed decisions based on data-driven insights.

Financial Analytics with Python (1h 6m): In this part of the course, participants will be introduced to financial analytics using Python, another popular programming language in data analysis. The module covers the basics of Python programming and demonstrates how to perform financial analytics tasks using Python libraries such as Pandas and Matplotlib.

Module 2: Financial Analytics with R

In this module, participants will dive deeper into financial analytics using the R programming language. Building upon the foundational knowledge acquired in Module 1, participants will explore advanced techniques and methodologies for analyzing financial data with R. Topics covered include time series analysis, risk modeling, portfolio optimization, and predictive analytics. Participants will learn how to leverage R's powerful libraries and functions to perform complex financial analytics tasks, allowing them to gain deeper insights into market trends, assess investment opportunities, and manage financial risks effectively. Through hands-on projects and case studies, participants will develop proficiency in using R for financial analytics and be well-equipped to apply these skills in real-world scenarios.

Financial Analytics in R - Intermediate (1h 28m): Building upon the foundational knowledge acquired in the beginners' module, this intermediate-level segment delves deeper into financial analytics techniques using R. Participants will learn advanced data manipulation, statistical modeling, and predictive analytics methods tailored for financial applications.

Financial Analytics in R - Advanced (1h 35m): This advanced module equips participants with the skills to tackle complex financial analytics challenges using R. Topics covered include time series analysis, risk modeling, and machine learning algorithms applied to financial datasets. Participants will gain proficiency in building sophisticated financial models and extracting actionable insights from data.

Through these modules, participants will develop a comprehensive understanding of financial analytics techniques and tools, enabling them to leverage data effectively for decision-making in finance-related roles. Whether you're a beginner looking to enter the field of financial analytics or an experienced professional seeking to enhance your skills, this course provides valuable knowledge and practical insights into the world of data-driven finance.

Sample Certificate

Requirements

Basic Understanding of Finance: While prior knowledge of finance is not mandatory, having a basic understanding of financial concepts such as financial statements, ratios, and investment principles will be beneficial for grasping the material covered in this course.
Familiarity with Programming Concepts: Familiarity with fundamental programming concepts such as variables, loops, and conditional statements will help you better understand the coding examples and exercises, especially in R and Python.
Statistical Knowledge: A basic understanding of statistics, including concepts such as mean, median, standard deviation, and probability distributions, will aid in comprehending the statistical techniques used in financial analytics.
Comfort with Mathematical Calculations: Being comfortable with mathematical calculations and equations will be advantageous, as financial analytics often involves quantitative analysis and mathematical modeling.
Proficiency in Excel: While not mandatory, proficiency in Microsoft Excel is recommended, as it is a widely used tool in finance and data analysis. Familiarity with Excel functions and formulas will facilitate your learning process, especially when transitioning to more advanced tools like R and Python.
Desire to Learn: Above all, having a strong desire to learn and a willingness to engage with complex financial and analytical concepts is essential for success in this course. Approach the material with curiosity and a growth mindset, and be prepared to actively participate in the learning process.

Target Audience

Finance Professionals: Individuals working in finance roles, such as financial analysts, investment bankers, portfolio managers, and risk analysts, who want to enhance their analytical skills and leverage data-driven insights in their decision-making processes.
Data Analysts: Professionals with a background in data analysis who are interested in applying their skills specifically to financial data and gaining a deeper understanding of finance-related concepts and techniques.
Students: Students pursuing degrees in finance, economics, accounting, data science, or related fields who want to supplement their academic studies with practical knowledge and hands-on experience in financial analytics.
Business Professionals: Professionals from various industries who are involved in financial planning, budgeting, forecasting, or strategic decision-making and wish to improve their analytical capabilities to better understand financial performance and trends.
Entrepreneurs and Business Owners: Entrepreneurs and business owners who want to gain insights into their company's financial performance, analyze market trends, and make informed decisions to drive growth and profitability.
Anyone Interested in Financial Analytics: Individuals with a keen interest in financial analytics, regardless of their professional background, who want to learn how to use data analysis techniques to interpret financial data, evaluate investment opportunities, and mitigate financial risks.

Course Ratings

Financial Analytics Course

It was very helpful. Got to learn about excel in detail. Financial analytics is very helpful in analysis process of data. Mean, median and mode and other financial tools are of great importance when we have to do analysis on big data. Financial analytics is the creation of ad hoc analysis to answer specific business questions and forecast possible future financial scenarios. Financial analysis software can speed up the creation of reports and present the data in an executive dashboard, a graphical presentation that is easier to read and interpret than a series of spreadsheets with pivot table.

Dinky A Patel
Financial analytics course

It gives ideas of statistical tools and clears the concept as how to use.it also provide grip to various concept such as trimmed mean,standard deviation,mode quartiles and various other function it also gives detailed analysis of each function and at the end of the program there were examples which were very helpful to understand concepts of this statistical tools and gives ideas of how to use all this practically...

Harshit Seheju
Financial Analytics Training

Wow, what a great course! Covers all of the basics in using Statistical functions in Excel. Now i feel intelligent! And very conversational in the stats of Excel. So take this course and you will also see what it can do for you.. You will not only be intelligent but also impress by yourself!, your friends and other professionals.

jAMES cLAY fIELDING
Glad that I enrolled

This is a good course to learn the basics of financial analysis using statistical tools in excel. Overall this course is quite good enough to get basic ideas on different statistical tools like- mean, geometric mean, standard deviation and many more. I'm quite happy that I got the chance to enroll for this course.

Rakibul Hossen