How to Make a Successful and Smooth Career Transition From Marketing to Data Science
Andre Gide, a French writer and Nobel Prize winner in Literature once said, “Man cannot discover new oceans unless he has the courage to lose sight of the shore.” This quote can act biblical, especially when you are aiming for a career transition. It is important to venture out into new domains, explore and confidently step up to pace up, be it in any domain.
Learning should never cease. Always, make sure you skill up and upgrade your knowledge base so that you can make sure you are not pulled behind. Particularly, when it comes to this highly competitive cat-and-mouse chase.
Coming to point, if you are working in the sales and marketing field and are aiming for a career transition into data science, how can you proceed? Come, we will walk you through some easy steps that can help you gain insights on this. But, to begin with, why data science? Is a career in data science worth it? Are you looking for a data science course in Bangalore, Kolkata, Mumbai, and other states according to your location? How can data science be used in sales? Or, marketing in general for that matter? Here, we will try to explore the answers to these questions.
Switching career to data science
Why data science because it is promising — offering you a lucrative career with a plethora of job opportunities. If you hail from a marketing field yourself, you might be knowing the abundant use of data to make smooth operations in business. Most importantly, being in the marketing and sales field you will be working in close association with big data analysts, who deal with a bulk amount of data. So, a transition or career switch from marketing into data science should be smooth and can happen gradually and naturally.
Where do you start?
Here are a few things that you should know before taking the big step of career transition. This works for most of all domains.
- Understanding the skill gap
Before making a career switch from marketing into data science, make sure you go through the job responsibilities. Also, analyze the skill gap. This allows you to pick a domain area in data science that goes well with your existing marketing skill-set. Besides, you will get an idea of the areas that require skilling up.
2. Do a thorough research
Get insights on the possible career prospects. Research on specific data science domains that goes well with marketing. Focus on the domain that you have narrowed down. Now, figure out the technical skills you require to excel in that field.
3. Have a knack for programming and math
Not that you need to be a coder. But, that said, having programming skill-set is vital when it comes to being a data scientist. Besides, brush up on your statistics and analytics skills too. Signing up for a data science certification course will be beneficial.
4. Learn Python and SQL
Study Python in depth if you aim to don data scientist roles. Also, focus on major concepts like data modeling and data cleaning using python. Also, you can make efforts to learn SQL. You can sign up for SQL boot camps for easier learning.
5. Study data visualization
To discover data trends, it is important to get trained in data visualization. Untapping and unveiling information by evaluating data trends is vital for businesses. Using data visualization tools (like Tableau) you can translate a bulk amount of data into interpretable forms such as graphs and charts.
6. Get exposure
To excel in any field or data science for that matter, you need to get industry exposure. This means you should be having relevant project experience working in real-time data science projects. This can boost your portfolio to greater detail. Also, network with people in the data science domain to get further exposure.
Identifying the differences
If you are aiming to merge marketing with data science, you should familiarize yourself with potential job opportunities. Such as, roles like marketing data scientist or marketing data analyst.
Just like a data analyst or business analyst, collection of datasets and analyzing the same are the main tasks of marketing data analysts too. Only that all their efforts will be centered relating to the marketing perspective. Marketing data analysts are particularly skilled in SQL and excel that equips them to deal with bulk data sets.
Coming to marketing data scientists, while marketing data analysts focus on data collection, marketing data scientists work in drawing interpretations from it. This is done based on exploratory data analysis, data visualization etc. To don these profiles, you should skill up technically such as — machine learning, Tableau and related data visualization tools, SQL and the like.
Now that you are familiar with important job profiles relating to data science in marketing, let us explore the scope of the field.
Scope of data science in marketing
According to Techrepublic, an online trade publication for IT professionals, from 2019 the percentage of hiring marketing data professionals in the majority of companies has risen up. Which means, there is an abundant scope of job opportunities that await you in the marketing domain, particularly under data science.
Reach of data science in marketing
Data science in marketing opens the door to performing the following key functions:
- Marketing budget optimization
Analyzing the average market spend based on data models. Just like spend, acquisition can also be monitored using these models. Budget distribution across various spheres and channels can be better managed using this.
2. Strategy for content marketing
Content marketing strategies which are data driven can be devised. This helps in getting better reach to the content. With a strong data reference, targeted consumers can be charted better.
3. Sentiment Analysis
Analyzing the customers based on their aptitudes and behavior have a great role to play in marketing. Sentiment Analysis helps perform this by evaluating customer responses to various instances like marketing campaigns.
4. Customer profiling
Another important aspect is profiling of audiences based on customer activity. The information collected based on customer interests can build profiling. Better understanding of customers helps generate efficient business strategies on moving forward.
In addition to all the scopes of marketing data science mentioned above, there are a few other potential advantages too. Such as:
- Identification of right marketing channels using time series models.
- Email campaign optimization and efficient offer prediction to attract customers.
- Data science insights to build an efficient strategy on social media engagement.
- Churn prediction and identification of customer patterns and prospects.
- Lead scoring and lead targeting by identifying potential customer value.
- Real-time analytics in marketing to understand customer behavior.
- Recommendation engines and predictive analysis.
Before making a career switch from marketing to data science understand the skill gaps and job responsibilities. There are many data science related job opportunities in marketing such as — marketing data analysts and data scientists that you can pursue after skilling up. The best way to get yourself trained is by signing up for online data science training programs.
E-learning platforms such as Skillslash offers tailor-made courses with expert training and certification. Signing up in Skillslash data science courses can enable you to get relevant project expertise in the marketing data science domain as well. Learning under expert supervision with a curriculum focused in industrial training can definitely help you pave a sound career in marketing data science. You can always make use of the Contact Us page and get in touch with the support team for a free career-focusing counseling session.