How to become a data scientist? Is data engineering in more demand than data science? Is it possible to get into machine learning for a data engineer? Is deep learning a critical skill for data science roles? What are the required skills for data engineering, data science, and deep learning jobs? – these are some of the top queries that we received in 2021 so far. In this post, we will try to decipher all those queries. Additionally, we will give you an overview of the data science careers, required skills, and job trends.
Overview of Data Science Career Path, Required Skills, and Jobs
What is Data Science?
Data Science is an interdisciplinary field focused on extracting meaningful insights for the use of business applications. The field of data science lies at the intersection of computer and information sciences, Mathematics, Statistics & modeling, and the understanding of business and contextual knowledge.
For anyone interested in studying or developing a career in data science, they need to have the following:
- some basic knowledge and skills in programming
- computer architecture
- data structures
- mathematical modeling
Once you have this understanding, you can develop more hands-on experience in a certain industry or across horizontal applications of data sciences.
There are a few other practical areas that surround data sciences and are a little more specialized in nature and studies such as operations research in industrial engineering, machine learning in computer sciences, and business analysis in MBA or MIS degrees. This tells us that there can be a number of paths and opportunities for anyone who is looking to focus on the area of Data Science.
What are potential jobs and career opportunities in Data Science?
Most of the data science graduates and professionals are hired for the following roles:
- data engineer
- data analyst
- AI/ML engineer
- software engineer
- business analytics professional
Data engineers focus on integrating, ingestion, and storage of massive datasets from multiple data sources either in real-time or in batch mode.
Machine Learning (ML) engineers are focused on developing models, algorithms, or closed-loop systems on these massive data sets.
Many software engineers work closely with data engineers or AI/ML engineers on developing systems, applications, or on business intelligence capabilities within the platforms and products.
Business analysts are more on developing analysis and assessment for day-to-day business operations or providing feedback to the technology teams on the effectiveness and business metrics that are important for the business.
Skills Needed to be successful in the field of Data Science
Here are a few skills that are critical for graduates and young professionals to have at different levels of seniority and experience to be successful in the field of data analytics. These skills complement the technical aptitude of professionals.
The main skills needed to be successful in data science are:
- Quantitative skills – Needed to develop models, analysis, algorithms and understanding of data, variable, correlations etc.
- IT and Solution Architecture Skills – These skills are related for wholistic view and solution design, development etc.
- Business acumen skills – These are important and needed for the business domain and technology overlay to solve the problems and challenges.
- Soft Skills – These are important for business analyst professionals and senior technology and product leaders for story telling and providing business and recommendation plans to the senior business leaders.
Here is an overview map on how the skills map against the level of data science roles and opportunities:
Job Opportunities for graduates and professionals of data sciences
- Data Engineer and Architect: Digital transformation is requiring companies to connect and transform data sources, data warehouses, databases in more cloud, hybrid environment (data streams, data events, data lakes, cloud databases etc.)
- Data/Business Analyst and Data Scientists: Taking the data and analyzing them, business and analytics report, to run business on day to day basis. Information Management, Risks, Customer marketing, operations, accounts, finance etc.
- AI/ML Engineer: Building powerful AI/ML models: Developing self learning and self-running systems. Recommendation engines. Knowledge of Amazon, Microsoft, IBM, Google developed AI/ML platforms and capabilities
Data Engineering and Data Science Salaries in India
As of January 2022, the average salary structure for a Data Engineer in India are somewhat like (according to payscale.com):
For a Data Scientist, the average annual salary looks like:
Data Engineering Jobs vs Data Science Jobs
What is Data Engineering?
Data engineering is a subset of data science, a comprehensive term that incorporates numerous fields of information related to working with data. Fundamentally, data science is tied in with getting data for analysis to deliver significant and valuable insights. The data can be additionally applied to offer some value for machine learning, BI, data stream analysis, or any other type of analytics.
Read Data Engineer vs Data Scientist – Background, Responsibilities, Skills, and Job Prospects.
Current Data Science Job Market
At present, the majority of job positions (around 70%) that are being advertised as data scientist positions, are actually data engineering roles. If you are a data scientist and you do SQL, Tableau and spend most of your time preparing data and data pipelines, then you are nothing more than a data engineer but with a data scientist title.
If you are a data scientist who wants to do ML modeling, taking a data engineer job might be boring for you. You sit there and write SQL all day long and think about containers, migrations, processes, and file types. A data engineer who is dedicated to only ML is called a machine learning engineer and he has a few more skills than a data engineer like putting an ML model into production.
Today it is really hard to find true data scientist jobs. Most real data scientist jobs have moved on to deep learning. Traditional ML-like Random Forest or basic algorithm-based Python modeling has moved to the cloud. That is, there are too many off-the-shelf cookie-cutter applications that just allow you to upload your data and run a few pre-built algorithms without requiring any other knowledge. Essentially, that’s what data scientists of that kind do.
Read the story of Nandan Mishra, who pursued Masters in Data Engineering in Germany with a Scholarship.
Deep Learning is the New Trend
What is Deep Learning?
Deep learning, also known as the deep neural network, is one of the approaches to machine learning. Other major approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks.
Deep Learning is a machine learning strategy that shows devices and computers how to do logical functioning. Deep learning gets its name from the way that it includes diving deep into numerous layers of the network, which additionally incorporates a hidden layer. The deeper you jump, the more intricate insights you remove. Read Deep Learning vs Machine Learning vs Artificial Intelligence.
Deep learning involves mathematical modeling, which can be thought of as a composition of simple blocks of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
Deep Learning Jobs
At present, any result that can be achieved using Random Forest, Gradient Boosting, or any other top algorithm can be replicated more or less using deep learning. Today, any real-world complex problem can only be solved using deep learning.
If you look at any complex problem that big-tier companies like Google have solved, there will be some deep learning in there somewhere. The one thing that is not clear is the title for data scientists who do deep learning exclusively instead of traditional machine learning. Deep learning scientist, or maybe deep learning engineer.
How to Get Data Engineering/Science and Deep Learning Jobs
More Data Engineering Jobs
Data engineers are in increasingly high demand compared to other data-driven professions. In a sense, this represents an evolution for the broader field.
When machine learning become hot 5-8 years ago, companies decided they need people that can make classifiers on data. But then frameworks like Tensorflow and PyTorch became really good, democratizing the ability to get started with deep learning and machine learning.
This commoditized the data modeling skillset.
Today, the bottleneck in helping companies get machine learning and modeling insights into production centers on data problems.
How do you annotate data? How do you process and clean data? How do you move it from A to B? How do you do this every day as quickly as possible?
Data Science Job Market will be Competitive
It is getting harder and harder to find work that does real ML and DL. 70 – 90% of the jobs are for data engineers but are titled Data Scientists.
Due to less demand for real ML and DL, the competition is really high for those jobs. I would actually make a bold claim that Machine Learning as we knew it circa 2010 is pretty much dead! It has been democratized, automated, packaged, and put into drop-down menus so even a marketing guy can run it.
Right now the push seems to be to get the data right and the only person that can do that is the data engineer! No one is worried about the ML portion because you could just connect your BI to cloud AI and even a high-level tech-savvy manager or marketing guy can run the show! lol. Or a data engineer for that matter!
Challenges with Data Scientist Jobs
So, if you are looking for data scientist jobs, competition is going to be tougher. There are going to be fewer positions available for what is looking to be an abundance of newcomers to the market trained to do data science.
There will always be a need for people that can effectively analyze and extract actionable insights from data. But they have to be good.
Downloading a pre-trained model off the Tensorflow website on the Iris dataset probably is no longer enough to get that data science job.
It’s clear, however, with the large number of ML engineer openings that companies often want a hybrid data practitioner: someone that can build and deploy models. Or, said more succinctly, someone who can use Tensorflow but can also build it from the source.
Scenario of Machine Learning Jobs
Another takeaway here is that there just aren’t that many ML research positions.
Machine learning research tends to get its fair share of hype because that’s where all the cutting-edge stuff happens, all the AlphaGo and GPT-3 and what-not.
However, you will probably find more of those kinds of roles at industry research labs that can afford to take capital-intensive bets for long stretches of time rather than at a seed-stage startup trying to demonstrate product-market fit to investors as it raises a Series A.
Deep Learning Jobs are Increasing
Deep Learning is an extremely exciting development that has sparked an AI revolution in many aspects of our lives and is the key technology behind the recent spectacular developments in fields such as biomedical signal analysis, image recognition, driverless cars, speech processing, and natural language processing.
Deep Learning and Natural Language Processing are the Hottest In-Demand Skills
Regardless of whether it is parking assistance through technology or face recognition at the air terminal, deep learning is fuelling a ton of automation in this day and age. Notwithstanding, deep learning’s importance can be connected most to the fact that our reality is creating dramatic amounts of data today, which needs structuring on a huge scale.
Natural language processing is another skill set with the AI discipline that is more in demand this year. A lot of startups and companies are looking for people with experience in speech recognition and NLP and hires a lot of new graduates as well.
What should be the Learning Roadmap for Getting Deep Learning Jobs
Here is a typical structured learning path:
Prerequisites (Month-0):
- Python for Data Science
- Statistics
- Data Presentation & Analysis
- Linear Regression
- Logistic Regression
- Decision Tree Algorithm
- K-fold Cross Validation Singular Value Decomposition
Month-1: Getting Comfortable with Text Data
- Text Mining
- Regular Expressions
- Text Preprocessing
- Exploratory Analysis of Text Data
- Extraction of Meta Features for Text Data
Month-2: Computational Linguistics and Word Vectors
- Extracting Linguistic Features
- Text Representation in Vector Space
- Topic Modeling
- Information Extraction
Month-3: Deep Learning for NLP
- Neural Networks
- Optimization Algorithms
- Recurrent Neural Networks (RNN)
- PyTorch
Month-4: Deep Learning Models for NLP
- RNNs for Text Classification
- Convolutional Neural Network (CNN) Models for NLP
Month-5: Sequential Modelling
- Language Modeling
- Sequence-to-Sequence Modeling
Month-6: Transfer Learning in NLP
- ULMFiT
- Transformers
- Pre-trainer Large Language Models
- Fine Tuning Pre-trained Models
Month-7: Chatbots and Audio Processing
- Chat Bots
- Audio Processing
Your objective should be getting prepared to work with very large datasets and solving for the complexity of long-form conversations. Check out our curated list of best online courses on Machine Learning and Deep Learning.
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