AI News, Aspiring Data Scientist? Here Are Some At Work Project Ideas

Aspiring Data Scientist? Here Are Some At Work Project Ideas

Do you find yourself wanting to move into Data Science but keep hearing 'get some data, analyze it, and you'll be fine...'?

few days ago, a member from our newsletter asked us our thoughts on question a reader on reddit.com/r/datascience asked regarding project ideas using data from their work for an aspiring data scientist.

At the moment, I have been entrusted with pretty much all of the company's data, including service records, material pricing, billing info, and even vehicle tracking data.

The person asking the question has a PhD in physics and has some of the necessary base skills for data science - programming, data analysis, and visualization.

- pick a problem the business has - are the marketing dollars working as best as they can - and then figuring out which Data Science techniques you can use to answer that question...)

However, as this person is currently in a job and wants to build an independent project to showcase their data science skills in the short term, the best approach is the Top Down approach.

The person asking the question's goal then is to find a way to help the business grow its profits only using data from one or more of these functional areas and data science techniques.

It's just that the effect is either longer term (happy customers eventually recommend other customers) or harder to measure (happy workforce helps to attract a better work force which makes the company better).

Since the person asking the question's boss is willing to let him/her use the data as long as the day-to-day tasks get done, we can safely assume that the boss will be happy to let this person work on a project for sometime though probably not forever.

A data scientist's job can be broken down at a high level into two areas - data engineering & asking questions and trying to answer them with data.

For the data engineering part, the somewhat general consensus is that 80% of the time of a data science project is spend on the 'data munging' part of it (e.g.

You can think of the 'data munging' part of the project as getting the data as it's currently stored into a nicely shaped, cleaned, checked, formatted, easily available data set that you can then throw into your modeling/visualization system.

As an example, let's look at A) marketing and sales and take a look at the full data science pipeline to see where things can be improved by asking the 5(6) W's and asking if each area can be improved:

Moving all of the disparate sales and marketing data sets that are perhaps are siloed in spreadsheets on various people's computers into a centralized online data store that will scale to the size needed 5 years from now...

Given the companies goals and aspirations, revamp the data collection process to capture even more data about the sales and marketing functions (this involves figuring out what to capture as well as how to accurately and efficiently capture it)...

Use Optimization techniques to maximize the number of views of company promotional material a prospective customer sees for a given dollar amount of promotional spend...

It's also important to remember that 80% of data science is generally thought to be data munging so while it may seem like the right thing to do is to jump into the modeling, it's much better to look at all the areas that can be improved and figure out where the highest value can be provided.

The great thing about the situation the person asking the question finds themselves in is that though their boss might not have a great deal of understanding of how to use data to make the company better / bring in more profit, they are willing and really want for the question asker to turn data into dollars.

And, because the boss is letting them try a project, it means that the communication lines are open and that constant dialogue should happen to make sure that the right problems are being tackled that benefit everyone involved - the boss looks great and the person asking the question will have developed data science skills they can showcase in more explicit data science contexts.

Aspiring Data Scientist? Here Are Some At Work Project Ideas

Do you find yourself wanting to move into Data Science but keep hearing 'get some data, analyze it, and you'll be fine...'?

few days ago, a member from our newsletter asked us our thoughts on question a reader on reddit.com/r/datascience asked regarding project ideas using data from their work for an aspiring data scientist.

At the moment, I have been entrusted with pretty much all of the company's data, including service records, material pricing, billing info, and even vehicle tracking data.

The person asking the question has a PhD in physics and has some of the necessary base skills for data science - programming, data analysis, and visualization.

- pick a problem the business has - are the marketing dollars working as best as they can - and then figuring out which Data Science techniques you can use to answer that question...)

However, as this person is currently in a job and wants to build an independent project to showcase their data science skills in the short term, the best approach is the Top Down approach.

The person asking the question's goal then is to find a way to help the business grow its profits only using data from one or more of these functional areas and data science techniques.

It's just that the effect is either longer term (happy customers eventually recommend other customers) or harder to measure (happy workforce helps to attract a better work force which makes the company better).

Since the person asking the question's boss is willing to let him/her use the data as long as the day-to-day tasks get done, we can safely assume that the boss will be happy to let this person work on a project for sometime though probably not forever.

A data scientist's job can be broken down at a high level into two areas - data engineering & asking questions and trying to answer them with data.

For the data engineering part, the somewhat general consensus is that 80% of the time of a data science project is spend on the 'data munging' part of it (e.g.

You can think of the 'data munging' part of the project as getting the data as it's currently stored into a nicely shaped, cleaned, checked, formatted, easily available data set that you can then throw into your modeling/visualization system.

As an example, let's look at A) marketing and sales and take a look at the full data science pipeline to see where things can be improved by asking the 5(6) W's and asking if each area can be improved:

Moving all of the disparate sales and marketing data sets that are perhaps are siloed in spreadsheets on various people's computers into a centralized online data store that will scale to the size needed 5 years from now...

Given the companies goals and aspirations, revamp the data collection process to capture even more data about the sales and marketing functions (this involves figuring out what to capture as well as how to accurately and efficiently capture it)...

Use Optimization techniques to maximize the number of views of company promotional material a prospective customer sees for a given dollar amount of promotional spend...

It's also important to remember that 80% of data science is generally thought to be data munging so while it may seem like the right thing to do is to jump into the modeling, it's much better to look at all the areas that can be improved and figure out where the highest value can be provided.

The great thing about the situation the person asking the question finds themselves in is that though their boss might not have a great deal of understanding of how to use data to make the company better / bring in more profit, they are willing and really want for the question asker to turn data into dollars.

And, because the boss is letting them try a project, it means that the communication lines are open and that constant dialogue should happen to make sure that the right problems are being tackled that benefit everyone involved - the boss looks great and the person asking the question will have developed data science skills they can showcase in more explicit data science contexts.

The Data Science Process

Turns out, Raj employs an incredibly helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.

You should ask questions like the following: In response to your questions, the VP Sales might reveal that they want to understand why certain segments of customers have bought less than expected.

Step 2: Collect the raw data needed for your problem Once you’ve defined the problem, you’ll need data to give you the insights needed to turn the problem around with a solution.

This part of the process involves thinking through what data you’ll need and finding ways to get that data, whether it’s querying internal databases, or purchasing external datasets.

You might find out that your company stores all of their sales data in a CRM or a customer relationship management software platform.You can export the CRM data in a CSV file for further analysis.

You’ll see errors that will corrupt your analysis: values set to null though they really are zero, duplicate values, and missing values.

You’ll want to check for the following common errors: You’ll need to look through aggregates of your file rows and columns and sample some test values to see if your values make sense.

You’ll have a fixed deadline for your data science project (your VP Sales is probably waiting on your analysis eagerly!), so you’ll have to prioritize your questions.

Step 5: Perform in-depth analysis This step of the process is where you’re going to have to apply your statistical, mathematical and technological knowledge and leverage all of the data science tools at your disposal to crunch the data and find every insight you can.

If you’d asked a lot of the right questions while framing your problem, you might realize that the company has been concentrating heavily on social media marketing efforts, with messaging that is aimed at younger audiences.

Throughout the data science process, your day-to-day will vary significantly depending on where you are–and you will definitely receive tasks that fall outside of this standard process!

It’s important to understand these steps if you want to systematically think about data science, and even more so if you’re looking to start a career in data science.

Even if you’re not looking to break into the field, your career in data science will only get better by getting back to the basics and understanding them thoroughly.

Unlocking the power of data in sales

Analytics plays an increasingly important role in B2B sales—and high-performing sales organizations take it to a new level to differentiate themselves from the also-rans.

Forward-thinking companies are using the growth of data analytics and artificial intelligence to expand the frontier of value creation for B2B sales and are generating remarkable results in lead generation, people management, cross-selling, and pricing (Exhibit 2).

Analytics is well-suited to improving the accuracy of lead generation and automating presales processes as companies use rich data sets to identify the right customer at the right time.

Internal data sources on the customer’s previous history are combined with rich external data such as news reports or social media to generate a “360 degree”

One IT services company used such big-data analytics to predict which leads were most likely to close—and found that established companies were better prospects than the start-ups it had been focusing on.

Several companies are experimenting with AI-enabled agents that use predictive analytics and natural-language processing to automate early lead-generation activities such as handling basic customer questions and automating initial presales questions.

Similar to the way analytics revolutionized baseball by revealing the true factors underlying wins, sales forces are using analytics to understand what drives sales success and to inform coverage, hiring, and training.

The result is that, over time, sales models become less effective and globally inconsistent, while resources are poorly allocated to accounts that require different types of sales strategies (e.g., grow versus retain).

Taking it a step further, some companies are integrating email, calendar, and CRM interaction data to identify which actions in the field correlate with success, particularly for technical sellers whose value is harder to assess.

A logistics company mined such historical ordering patterns to identify cross-sell opportunities within its customer base and then built tailored microcampaigns around those opportunities.

One software company was able to increase return on sales by more than 20 percent by providing pricing information based on statistically similar deals to the field.

second challenge is setting the price of new products or solutions, particularly when there is no comparable product on the market or market conditions shift rapidly.

One online media company used dynamic pricing to generate real-time quotes for classified space and was able to generate 5 percent more revenue.

By embedding the analytics within a test-and-learn approach, it continued to improve and reap the benefits of higher pricing, greater volume, and increased customer satisfaction.

Although this move cut the number of potential sales by 10 percent, it grew the average size of each sale by 25 percent, leading to an overall increase in revenue.

Quick and dirty analysis will often surface the best options to start with, though additional work may be necessary to evaluate trickier issues like scale and security.

Yet, by cleverly implementing machine-learning approaches and complementing internal data with external sources, leading companies have been able to extract valuable insights even from poor data.

This means hiring people with advanced skills in statistics and machine learning but complementing them with experienced sales-analytics experts who can translate the insights into actions for the field.

Fourth, they embed analytics within defined sales workflows to ensure insights are available at the time they are most valuable, e.g., integrating deal-scoring algorithms into sales tools and related processes, such as deal approvals, so salespeople can use that information during negotiations.

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