Financial Modeling vs Data Science – Which Course Is Better?
Have you heard about the opportunities for students of Financial Modeling vs Data Science learners? If not, learn about it today in detail. This article will shed light on the concept of financial modeling and data science along with the similarities, nature, and career scope of both courses. Though both the tools focus on data insights, the benefits they offer the learners are different and the eligibility criterion for availing of the educational programs and entering relevant fields vary. Take time to decide on picking financial modeling or data science certifications today!
Financial Modeling
It is a form of financial analysis that involves the use of an Excel sheet to create a forecast of the company’s future based on its history. This process involves the creation of a model to help the management make proper decisions from a report using data from cash flow statements, balance sheets, income statements, and other financials of the company.
Data Science
It deals with the extraction of meaningful insights from the study of data. The data analysis is done using the practices and principles from the fields of statistics, mathematics, computer engineering, and artificial intelligence. This concept is introduced to let the data scientists seek answers to questions like why did the analysis produce this result? This field is a combination of various tools, methods, and technologies to provide insights to the company to help them make better decisions.
Financial Modeling vs Data Science – Certifications Details
What are financial modeling certifications and data science certifications?
As the title suggests, these are courses that educate and train the students to carry out functions related to financial modeling and data science as per the chosen concept. These have curricula to enlighten the learners on various concepts of the tools alongside letting them experience real-life scenarios through live projects. Completion of the courses is followed by the issuing of a certificate by the educational entity indicating the student has successfully obtained knowledge and skills about the selected methods and is ready to work as a professional in the relevant field.
Advantages of availing of a financial modeling course
The main reason for joining any institute for an educational program is to acquire skills and knowledge about the respective field. Similarly, individuals seek a financial modeling certification to gain the below-mentioned benefits.
- Practical skills
- Credibility
- Industry best practices
- Demonstrates your hunger for self-improvement
- Different Modeling Methods
- Master at Excel
- Get the limelight
- Increased confidence
Credibility: This is no brainer; any student from a top-notch institute like IIM SKILLS has more credibility than the rest. Once you mention the institute name in your resume, it automatically improves the value of your portfolio. Also, your ability to discuss and explain various financial models is sure to impress your boss, colleagues, and interviewer. I.e., established credibility. An employer is sure to hire a candidate with the ability to create quantitative financial models compared to those who can’t.
Industry best practices: When you avail of a financial modeling course from a leading ed-tech, you will be exposed to the best practices. The program will give you an opportunity to get to know the best practices in the industry through case studies and practical assignments. Some of the training entities also involve you in live projects to gain as much knowledge as possible. You will gain knowledge about various globally followed skills implemented across a variety of industries. Not just that, you will have exposure to a range of methods and tools used in financial modeling accepted over the world.
Different Modeling Methods: Joining a course from a college or institute will get you exposed to at least one financial model or gain information about a variety of models. It indicates that you will acquire the skills and data to build financial models.
Master at Excel: As explained previously, financial modeling revolves around spreadsheets. A certification program will have modules that focus on teaching the students to use Excel and become a master at it. Most entities dedicate separate sessions and modules for educating the learners on using spreadsheets in an efficient way. Accurate predictions are a result of effective Excel skills; no person can build efficient models on spreadsheets if they lack the information and skills to use Excel. This also indicates that a master of Excel can make better decisions with the help of accurate outcomes.
Get the limelight: Believe it or not, many employers value certifications. A CV with certification in it will attract companies that are keen on recruiting analysts for their business growth. The candidates without certification may lag behind and struggle for opportunities but the individuals with course completion certificates enjoy exploring several offers from well-established companies.
Increased confidence: Certification programs will help the student understand the concept of building financial models and utilizing their decision-making skills regarding the company’s performance. In simple words, you will get the confidence to apply for jobs and work without fear, unlike many candidates with low confidence.
Employers’ reasons to recruit a certified financial analyst
Every company has criteria to pick candidates and put them through a screening process before sending them an offer letter. Every role has a different set of expectations for the candidates. Potential financial analysts should have the below faculties.
- Qualitative and quantitative skills
- Beginner’s knowledge
- Intermediate knowledge
- Advanced knowledge
Qualitative and quantitative skills: Though the financial modeling revolves around figures related to the company’s history. It also dwells on the interpretation of data, meaning the candidate must have the ability to analyze the outcomes and thereby forecast the future of the entity. Quantitative skills require the employee to consider the company’s profits, expenses, and related numbers to create a reliable financial model. The individual must also possess strong verbal and written communication skills to share their findings with the relevant teams. Hands-on experience in building charts and using Excel are mandatory skills on a resume.
Beginner’s knowledge: Basic understanding and experience in handling accounting, financial forecasting, and creating financial statements are important to get selected from hundreds of candidates. The financial modeling courses train the trainee to interpret and create templates for financial modeling. A basic knowledge of stock markets and financial markets along with the related assets such as mutual funds is crucial to deal with the company’s data.
Intermediate knowledge: The ability to use Excel for the creation of financial statements and financial models is appreciated as they permit the candidate to perform better among others. Knowledge about credit ratings, appraisals, and financial analysis is a plus if you want to be hired for a senior position. The ability to break circulation loops related to Macro along with understanding the difference between financial modeling and other models is helpful to stand out among colleagues. This is said so because management can directly hire and send you to the floor to start serving them, unlike people with limited skills that force the company to incur training costs on them.
Advanced knowledge: Discounted cash flow (DCF) is one of the common financial models used in the industry. So a skill to perform valuation by using it and comparable analysis can assist in stepping up as a financial analyst. If you are not aware of making estimations for merger transactions, get yourself upskilled. Playing with the options to create charts such as waterfall charts, Gantt charts, football field charts, and radar charts will make the life of management easy as they can avail of your skills instead of allocating the work to others. Building assumptions associated with takeover premiums and synergies will give you a higher probability to get promoted.
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Trending financial modeling courses in India
If you are determined to enter the field of valuation, pick any one course from the below list.
- Online Financial Modeling Course by IIM SKILLS
- FMVA which stands for Financial Modeling and Valuation Analyst offered by Corporate Finance Institute
- Udemy’s Beginner to Pro in Excel related to financial modeling course
- Financial Modeling and Valuation by The Wall Street School
- Certification in Online Financial Modeling by NSE
Career prospects of financial modeling
Individuals who complete a course in financial modeling are provided with a variety of job roles. Some of them are listed below. It is important to note that the curricula and training sessions of a program define the range of opportunities.
- Financial analyst
- Investment banking
- Corporate finance
- Accounting
- Wealth management
- Commercial banking
- Equity Research
- Real Estate
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Is it really Financial Modeling vs Data Science?
To know the answer, you need to first understand the advantages and job opportunities for both courses and then decide if it’s Financial Modeling vs Data Science or not.
Advantages of availing of a data science course and skills searched by employers in data scientists
Learning the benefits of data science courses makes it easy to know if Financial Modeling vs Data Science is real. Come, let’s read the advantages of joining a data course certification program. These points also explain the reasons employers hire candidates to perform data science.
- Career boost
- Up-to-date with Latest Industry Trends
- Showcase your expertise
- Portray your commitment
- Knowledge about Latest Data Science Techniques and Tools
- Structured course
- Cutting-edge skills
- Networking
Career boost: Many people don’t even know the right things to do to thrive in their respective fields. It means the competition in the data science field is lower for you. Once you choose an upskilling program, you will stand out among others. Employers love to shortlist candidates based on certifications as they explain the ability of the individual to use knowledge, skills and comprehend data.
Up-to-date with Latest Industry Trends: It is a fact that data science is an ever-evolving stream. It means picking the newest course will expose you to the latest trends and changes in the Industry. That being said, this can only be achieved if you join an accredited college as they take up the charge to keep you updated on the standards. Grasping fresh skills will allow you to expand your knowledge base and that can be done if you are a student of a well-established institute. This also means high-quality instructors can make your learning journey a cakewalk.
Showcase your expertise: Adding your achievements, awards, and certifications to your resume will help the potential employer understand your level of expertise. In a way, it lets the companies believe that you have the required skill set to become a data scientist. Credentials are magic weapons that make others’ profiles vanish in front of your CV because many top firms look for achievements in a candidate’s profile. Any individual with a desire to become a data scientist in a reputed company must avail of courses that benefit them. You may also be selected for roles like data analyst or consultant depending on the level of your expertise. What are you waiting for? Start hunting for a reliable college to pursue a data science program now
Portray your commitment: Isn’t it simple to understand that a person who pursues certification programs in data science is way more serious than those who don’t? Yes! This is why upskilling is required to develop your professional path. This is another striking resemblance to Financial modeling course benefit and confirms the falsehood of Financial Modeling vs Data Science.Your potential employers will understand that you have more skills, a higher level of knowledge, and dedication. There is cut-throat competition in the market, if you are keen on cracking an interview, then showcase your commitment through acquiring more skills, information, and achievements.
Knowledge about Latest Data Science Techniques and Tools: Most companies choose candidates with data science as a background as a data scientist has more analytical skills to study the data and identify trends. This point indirectly points to an individual’s patience, resources, and time to create appropriate solutions for business growth. The ability to play with tools like Jupyter notebooks for data analysis and manipulation on a live platform is a plus as it displays the level of expertise of the candidate; The tools and techniques used in companies lead to better decision-making as they facilitate easier understanding of the impact of changes on businesses.
Structured course: Self-learning may appear best but the fact is a mentor with a plan to guide you in reaching your career goals is a boon. Purchasing or borrowing study material from random people with the intention to become an efficient data scientist may not serve the purse, unlike students of reputed institutes who follow a structure for learning. A structured program will take the learner through a series of steps to give the required knowledge on various aspects of data science such as usage of statistical software.
Cutting-edge skills: As mentioned earlier, data science is a booming field, and updating your technical skills will secure your present as well as future professional life. Certifications carry a high value in any field. You will have a competitive edge over others on obtaining a data science certification. Good programs will include training crucial skills to the students which will help them stay updated with the latest industry standards. You will have the scope to understand data-science-related algorithms and their application through well-structured courses and comprehensive curricula.
Networking: If you want to skyrocket your chances in data science, the first thing to do is avail a course and the second thing is to get in touch with other data scientists. A course will give you the opportunity to interact with like-minded people and thereby increase your network. Sharing knowledge and helping each other in the application of skills will let you become an efficient data scientist.
Trending data science courses in India
There are many courses in Data Science in the nation. However, the most happening and fruitful ones are shared below to let you choose the best!
- Full Stack Data Science Course – Almabetter
- Udemy’s Python For Data Science and Machine Learning
- Data Science Certification Masters Program – Edureka
- IBM Profession Data Science Course – Coursera
- Scaler’s Data Science and Machine Learning Program
Career prospects of Data Science
There are many points to prove that Financial Modeling vs Data Science is not real, of which the career opportunities are one. Any individual who chooses Data Science Certifications over self-study will have the chance to become one of the below roles. Individuals who choose financial modeling will have knowledge related to finance whereas people who pick data science have faculties to deal with a company’s overall data using various tools from fields. Therefore, Financial Modeling vs Data Science; competition is unreal. The job opportunities differ from each other and are highly rewarding in their respective fields.
- Data Scientist
- Data Analyst
- Data Engineer
- Data Architect
- Business Intelligence Analyst
- Statistician
- Machine Learning Engineer
FAQs on Financial Modeling vs Data Science
1. What makes people see competition between Financial Modeling and Data Science ?
The main reason for viewing them as competitors is their common nature to derive insights from the company’s data. But the truth is they have their set of applications and advantages in their respective fields which makes the statement false.
2. Do I get to learn more skills from Data Science than from Financial Modeling?
In one way, yes. You get a chance to be exposed to a range of other fields in data science; statistics, mathematics, computer engineering, and artificial intelligence. That being said, the belief that both streams are competitive with each other is a fallacy; Financial Modeling vs Data Science. Because financial modeling deals with a variety of models, assumptions, and related tools like Excel sheets are absent in Data Science. Both fields have different applications and uses in the industries and are equally beneficial to the learners.
3. What other fields does Data Science involve?
Data Science derives insights from the company’s data using statistics, mathematics, computer engineering, and artificial intelligence.
Conclusion on Financial Modeling vs Data Science
Financial modeling and Data Science both involve finding insights from the data. However, the former method revolves around the analysis of a company’s financial performance and assumptions to build models to assist decision-making for the management and stakeholders. Whereas the latter uses various fields to handle data which are statistics, mathematics, computer engineering, and artificial intelligence to help the data scientists obtain answers to a variety of questions related to the company’s operations and then lead to making changes in weak areas for increased profits and business growth.
The availability of certification programs in these fields is fruitful as they open doors for a variety of opportunities for learners. Many consider it Financial Modeling vs Data Science. But that isn’t true because though both are meant to extract data insights to improve a company’s performance, they both have unique aspects that create many employment opportunities for the students.
For example, students of Financial modeling obtain skills and knowledge related to creation of various models and get to work in a range of streams; Investment banking, Corporate finance, Accounting, Wealth management, Commercial banking, Equity Research, and Real Estate whereas individuals who obtain a degree in data science can become a Data Scientist, Data Analyst, Data Engineer, Data Architect, Business Intelligence Analyst, Statistician, or a Machine Learning Engineer who integrates principles from different domains (statistics, mathematics, computer engineering, and artificial intelligence) for the company’s benefit.
In simple words, both streams have their own importance and allow passionate people to thrive in their respective fields. It is not Financial Modeling vs Data Science if one aims to advance in financial modeling or data science alone.
