Apps which helps in financial planning

 APPS FOR FINANCIAL PLANNING AND MANAGEMENT

Hi all readers, today we are gonna discuss financial planning and management apps. In today’s world, in every corner from a child to the elderly, everyone is having their own personal smartphone so it’s easy for everyone to access the internet and the advantages provided by the internet through resources and helping everyone with the help of varied applications. So typically, here we are going to have a brief summary of the financial planning and management applications. So here the smartphone comes in handy which can help manage our money through applications. It saves our time as well as our money too in terms of fees to be paid to an advisor and we don’t need to ask and travel somewhere else.

A person who is either salaried, self-employed or doing a business then it’s very difficult to earn money and the other way spending it is very easy. In today’s world of ever-increasing expenses/Inflation, it’s not only essential for all of us to keep an eye on all the incomes and expenses but also we must keep track of all our investments which would help in better money management. But the fact is that none of us has time to track income and expenses.

This is where our smartphones can help us to better manage our money. Multiple money management apps in the market automatically keep track of all our incomes and expenses as well as our investments and give us financial advice. These apps act as personal finance managers and help us in identifying the areas where our expenses can be reduced and can also suggest on best investment options.

In this era, where everyone is so close to their smartphones more than their loved ones even. So it has been very easy to manage and plan our savings and expenses through our smartphones with the help of these applications. So these apps are well capable of suggesting to us everything which a financial advisor can suggest. So we don’t need to visit an advisor in managing our money and paying them too, as our smartphone is doing for us for no cost.

Without wasting a lot of time, let’s see some of the available apps in the market which can help in financial management and planning as well:-

1. Mint

• It has all in one facility, so no need for multiple apps

• Tracks our cash flow with ease

• Saves smarter with custom budgets

• Saves time as well as our money by keeping track of finances in one place

• Bill pay reminders so you don’t need to worry about late fees

 It can even track bank accounts, credit cards, debit cards, and investments too.

 2. You Need a Budget

• You need a Budget is a fantastic application software for setting a budget and sticking to it.

• It is very properly developed and can give clear reports on savings, expenses, and investments.

• It also enriches users by providing resources and articles to enhance the knowledge of their users on financial planning and management.

3. Expensify

• It’s a software company that helps in the development of expense management systems for personal and business usage.

• A venture capital called “Expensify ventures” is operated by Expensify only.

• It does all of our pre-accounting in one app.

• It tracks all our expenses, generates invoices for us, pays bills, collects payments, etc.

4. Budget Boss

• It makes a budget plan for us on how to save and make expenses in a positive approach.

• This application helps us in the projection of the budget for the future.

• It can create a pdf report on our budget planning and others too.

• And additionally, we can sync with our calendar for bill reminders.

There are several other applications too which we can’t mention here all. So that’s all.

As all other applications also work in similar ways.

Thanks all for your time!

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The Data Science life cycle

 The life cycle of Data Science, it is clear from the name that we are going to discuss How a Data Science project is planned, executed, and completed. 

This completely depends on the type of project but yeah, we can see the basic steps involved in a Data Science Project.

According to me, the following are the steps involved: 

1. Planning
2. Data Acquisition
3. Data Preparation
4. Data Analysis
5. Model making
6. Improving the Accuracy
7. Report Making or Story Telling

again continuing from step 1 to step 7.

Now let's discuss these steps in detail.

1. Planning: The first step in which we plan our project from start to end that -

•  In how many sprints we are going to divide our project?
• All these above-mentioned steps will repeat in every sprint.
• We also plan for tools and technologies we are going to use in the project.
• Financial planning is also done here.
• Generally, Managers and Team Leads take these decisions.

2. Data Acquisition: The second step of the project.

• We know that data are of three types: Structured, Semi-structured, Non-structured. So according to the data, we use ingestion techniques.
• Also after knowing the types of data, we have to know that data is STREAMLINE or BATCH. If the data is streamlined means we have to process that data in real-time and if the data is batch then we can process it later according to our need. we can schedule it according to our needs.
• Data Engineers are mainly involved here. 

3. Data Preparation: The most important step of the project.

• The most important step is because, IF GARBAGE GO IN, GARBAGE COMES OUT. This step ensures that what we are going to feed our model. 
• The data we are getting either is structured or semi-structured or non-structured, either is streamline or batch, we have to prepare our data for the model. Don't get confused here that structured data are in the correct format for the model directly, structured data means they are in tabular form, that shit. 
• There is no clear bifurcation here that who will prepare data. Generally, both Data Engineers and Data Analysts work here.
• The work of the Data Engineer here is to make data available every time for the process, and the Data Analyst process the data.
• The data processing generally involve these main steps:

1. Data cleansing
2. Data types correction
3. Tackling missing values
4. Keeping important columns for analysis and model making

4. Data Analysis: The second most important step of the project.

• This is the step where we understand our data.
• We understand the important columns(Features) which can contribute to predicting Labels.
• Here, we find different insights which give us information about the business that what has happened.
• Here we can get some visualizations that can give many answers to the business questions.
• This is the very step where you talk to the data and decide which models will be best suitable for the data.
• This can be done by both Data Analyst or Data Scientist.

JOB RESPONSIBILITIES AS DATA SCIENTIST

5. Model Building: The step of Data Scientist.

• This is the very step for which we are doing all the previous two steps ie. Data Preparation and Data Analysis.
• If the previous two steps are not accurate then this step is also going to give you the wrong answer.
• Here we give the data to the model and then we predict the result.
• There are 1000s of models, so which model are we going to feed our data?
• This totally depends on the types of data we have is either falling into Classification or Regression or Time Series Data and then according to that we use the Machine Learning model to predict the output.

6. Improving the Accuracy: Important step after Model Building

• Only developing a model is not enough, after its development, we have to check its accuracy by using different Accuracy Metrics.
• If the accuracy is satisfactory, then your work is done. But this is not that easy.
• You need to backpropagate and check the data, check the parameters, and do some hyperparameter tuning. 
• After tuning the model we check the accuracy, and if not satisfactory and we repeat and do more hyperparameter tuning and then again check the accuracy.

7. Report Making: Story Telling time.

• This is the step in which you present your findings to the board of directors. 
• This is the step where you can make them feel that they know about their business a little more today.
• The way of storytelling matters much. 

Then we repeat all these steps for the next sprint of that project.

Make clear that one project can be divided into many sprints and in all these sprints we repeat these 7-8 steps. 

After completion of all the sprints then again we overall repeat these 7-8 steps and then check the accuracy and then we can say that the one Data Science project is complete.

I hope you understand the Life Cycle of Data Science. 

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