Data Science and Machine Learning Difference
06/11/20 by Motion Recruitment
With the need for Data Science and Machine Learning professionals increasing by up to 50% across several industries in 2020, the data science and machine learning field has never been hotter. We sat down with one of Tech in Motion's Timmy Award winners, Lacey Plache, to discuss the importance of a data science team versus a machine learning team to an organization.
Data science helps businesses identify opportunities from analysis of their data, make sense of their big data, design data modeling processes that provide valuable insights and provide roadmaps by predicting future outcomes.
Machine Learning helps businesses become smarter by creating algorithms that provide the ability to learn from their own data and automate certain processes, whether that has to do with reporting of the amplification of the data itself.
Last we are discussing about upcoming tech trends in data science and machine learning in 2020.
Lacey Plache was the 2019 Timmy Award winner for the Best Tech Manager, Judge's Choice Award. Lacey is an esteemed Data Scientist who has been pivotal in establishing and building out data teams from scratch at both Edmunds.com as the Chief Economist and at PatientPop as the VP of Data. Currently, she is the VP of Data Science at Age of Learning. Here's what Lacey had to say about ongoing Data Science and Machine Learning Trends leading up to today and beyond.
1. How have you seen the role of Data Science vs. Machine Learning develop throughout your career?
The implementation and analysis of data has transformed from manual data collection and entry to automation and machine learning. In my own experience, I’ve seen it develop drastically throughout my education and career.
To give you some perspective around the difference between data science and machine learning, the first data I ever analyzed outside of a class assignment was for my PhD. thesis. I was an economic historian and my topic was centered around how the French stock market supported industrialization in 1850's France. To get this data, I had to use old newspapers and investment guides from that time period by traveling to the French National Archives in Paris. I hand-entered each data point into Excel files and then built a database in Microsoft Access. The resulting analysis disproved popular beliefs that the stock market did not support emerging industrial companies and provided strong support for the power of markets to evolve to meet investment needs. Trend Identified: A Shift From Macro to Micro-Economic Trends.
At Edmunds, we originally focused our strategy on macroeconomic trends - what’s happening in the economy that is affecting the auto industry looking at supply and demand. However, what’s interesting to me as a micro-economist is how consumers behave, and I realized we had this wealth of data looking at what consumers are doing when they come to our website such as how they are engaging with advertisements and content, how they convert, and how we, as a two-sided platform, are making the connection between auto dealers and car buyers.
Therefore, the opportunity came up to create a customer journey team. What is the car buying journey? What digital touch points do consumers interact with? From there, it blossomed into an analytics department. We needed a full-blown team focused on advanced analytics and data science to not just look at the customer journey, but a whole range of problems that can be addressed inside the company. So that was my first leap into data science.
2. Is there a difference between Data Science vs Machine Learning teams?
The data team defines our strategy and administers how we acquire and analyze data to then draw insights that are relevant and usable for the company. From that information, we can identify what could translate into a product or decision.
The Machine Learning team build on that by adding products and building out different elements of the journey. We use what we learn to optimize products with the data we have regarding the whole customer journey. To adapt our model, we need to know how to find customers, who can benefit from using our tools, and how to engage them and give them a good experience to ensure they want to stay our partners. How do we make our product even better?
For a successful data team or machine learning team, both data scientists and analysts are important. Data analysts allow us to understand adoption rates and performance metrics for our products, while data scientists take that to the next level with models that predict outcomes such as which customers will turn or what product performances will be. These models also identify what factors are most important to drive outcomes. Knowing this helps us optimize both our customer funnel and our products' functionality.
3. What is the hiring demand for Data Scientists and Machine Learning Professionals?
Specifically in Los Angeles, there is a high demand for data science positions, but a low supply of highly skilled candidates to fill them. I see both genders applying without sufficient experience especially for senior positions. There are many more men who apply than women, but I have hired many talented women in data science over the years.
There are fewer women in the Machine Learning field because there are fewer women in STEM. Thus, I believe a large part of the issue starts with who decides to go into programs for data science to begin with. Once women get into the field, mentor-ship and even peer support in the broader community could be an issue. With the field dominated by men, especially at the senior levels, there are fewer role models for women, and it may be harder to network.
So, I would ask: How can we get more women into data science and machine learning programs? How young do we have to start encouraging the right path? How can we welcome the women who do go into data science into the existing community and make them feel that they belong there?
4. How would you describe the current market conditions of data and the tech industry in LA?
LA has become quite the hot spot and viable space for tech in the past 5-10 years seeing the opportunities and the kinds of companies we have here. There’s also a diversification that’s happening where people can work remotely allowing companies to tap in the talent all over the US and globe even by outsourcing by having remote workers or other offices.
Because of how tech is maturing in general, you’re getting companies who can build upon what exists. For example, Uber couldn’t exist without the smart phone. Expect to see further developments in AI because data is so readily available and quickly analyzed. There will be more smart products and activities adding to efficiency. In several large and small ways, companies can repackage what’s existed, extend it, and make it into something new. That is really shaping how tech works.
Then there’s the role of data where it allows us to understand how different tech is working. Moving beyond into the wire world, IoT is tracking all kinds of physical activity and behaviors. This data allows us to understand what’s going on in the world, what people are doing, and how they’re interacting. We can optimize on so many different fronts: what experiences people are having, how products are functioning, and how different tools are working.
Data Science and Machine learning professionals are among the highest in demand, and we can only expect this trend to continue as we move toward a more data-driven society. If you're interested in new opportunities, helpful resources or Data Science and Machine Learning webcasts, check out our job openings or view further tech market insights below.