“The goal is to turn data into information, and information into insight.” – By Carly Fiorina, ex CEO of Hewlett-Packard.
Since the dawn of the digital age, the data that has been generated will last and yield results for many lifetimes. Most companies have been reengineering their work structure and focusing on exploiting the available data to boost their revenue. Data is the new gold, and it is natural to be mesmerized by the lucrative fields mining this gold.
Now answer these questions. Are you passionate about data? Do you want to become a data scientist but don’t know where to begin? For those whose answer is a simple yes to both these questions, here is our step-by-step guide for aspiring Data scientists.
What is Data Science, and what does a Data Scientist do?
Data Science is the buzzword of the decade. Data has dominated the technological revolution for as long as one can remember.
Data science was once stated as the hottest job of the 21st century by Harvard. But now, due to the massive shift to data and technological dependencies, this job role has emerged as more of a necessity than a choice for the companies regardless of the sector.
Data Science helps in increasing the revenue generated by a company by better utilizing the available data. It helps reveal, analyze, and understand the hidden business trends, customer reviews and purchase tendencies, and much more. The vastness of its applications in every industry is what makes it a highly valuable field.
To put it into simpler terms, Data Science is a field that uses methods, techniques, algorithms, and processes to extract insightful information from data that may exist in structured or unstructured form. Data Science’s core principles overlap with different fields, but there’s no need to be confused. The various related fields are Data Mining, Machine Learning, Deep Learning, Natural Language Processing, and many more.
Pinning the exact definition of a Data Scientist is hard as this field is vaguely defined. Generally, the work done by a Data Scientist involves obtaining data, preprocessing and cleaning the data and making it comprehensible, integrating and storing the data, performing exploratory data analysis on the data obtained, applying data science techniques(machine learning, artificial intelligence, etc.), finding appropriate methods and algorithms to the data and yield and visualize the result to make it universally understandable.
The necessary skills to build a Data Scientist’s profile are business intelligence, statistical knowledge, probability, technical skills, data structure, data visualization, and communication. One must be adept in all these skills to build his/her/their career as a data scientist.
Skills/Tools/Technologies needed for a Data Scientist
Now that you have got a little bit of idea about the tasks of a Data Scientist, let us dive deeper and discuss the skills you require to get started.
Technical Skills- SQL or any other Database Managing language
SQL is a programming language designed specifically for storing, manipulating, and retrieving data from databases. You need efficiency in SQL as it forms the foundation of Data Science.
Technical Skills- Programming languages such as Python or R Programming
Python is an object-oriented programming language that is majorly used because of its versatility. This programming language is easy to learn and it is easier to work with as one can import the data directly into our file and structure it into datasets accordingly. There are provisions for importing SQL tables also.
R programming is a bit difficult to understand but goes a long way in terms of statistical programming. It is preferred by most data scientists.
Technical Skills- Hadoop
The large volume of data makes it almost impossible to derive conclusions. Thus, this technical skill- Hadoop- comes in handy when dealing with huge amounts of data. Handling, sharing, and communicating data with different servers become easy.
Thus, though this technology is a necessity, it is a highly desired quality for a Data Scientist.
Technical Skills- Apache Spark
Engineered from the bottom-up for performance, Spark can be 100x faster than Hadoop for large-scale data processing by exploiting memory computing and other optimizations. Spark is also known for fault tolerance.
Machine Learning and Artificial Intelligence
Machine Learning knowledge helps to solve different data science problems that are based on predictions of major algorithms. Thus, one should be able to apply the basic as well as advanced machine learning techniques to solve challenging Data Science problems.
Data Visualization
The result that is so obtained after pre-processing, cleaning, structuring, manipulating, and applying algorithms, must be visualized by using tools like PowerBI, Matplotlib, Tableau, etc. The visualization makes the data universally understandable so that people from non-specific, unrelated, and non-technical domains can comprehend them and make decisions based on the insights thus acquired.
Business Shrewdness
A data scientist must be able to derive conclusions and business suggestions to profit the organization. For this sole purpose, they must have a sharp sense of business. This is thus a critical skill for a Data Scientist.
Communication Skills
It is believed that Data Science is the art of telling stories via data. As mentioned earlier, a data scientist must have good communication and representation skills so that people from all domains can understand and make decisions based on the findings of the Data Scientist.
Our Course/Site Recommendations:
1. Data Science Specialization — JHU @ Coursera (Beginner)
This course is the best that is out there in the field of data science. It starts with the basics and covers all the concepts needed to understand the application of your knowledge. It has both theory and application in just the right proportions.
Skills You Will Gain:
- Github
- Machine Learning
- R Programming
- Regression Analysis
- Data Science
- Rstudio
- Data Analysis
- Debugging
- Data Manipulation
- Regular Expression (REGEX)
- Data Cleansing
- Cluster Analysis
2. Applied Data Science with Python Specialization — UMich @ Coursera (Intermediate)
This course is preferable for you if you already have some idea about R programming language and statistics. Though this series does not cover the statistics required for understanding various machine learning algorithms, it does provide the learner with an excellent introduction to the algorithms and a comprehensive breakdown of their applications.
Skills You Will Gain:
- Text Mining
- Python Programming
- Pandas
- Matplotlib
- Numpy
- Data Cleansing
- Data Virtualization
- Data Visualization (DataViz)
- Machine Learning (ML) Algorithms
- Machine Learning
- Scikit-Learn
- Natural Language Toolkit (NLTK)
3. CS109 Data Science (Intermediate)
This course is recommended for you if you have basic knowledge of Python and the functioning of Data Science libraries. The lack of an interactive platform does not make this course lose its charm. The course consists of a list of videos, lecture slides, lab videos, and a notebook.
Skills/Knowledge You Will Gain:
- Web Scraping
- Regular Expressions
- Data Reshaping
- Data Cleanup
- Pandas
- Data Analysis
- SQL
- Statistical Models
- Bias and Regression
- Classification
- kNN
- Cross-Validation
- Dimensionality Reduction
- PCA
- MDS
- SVM
- Evaluation
- Decision Trees
- Random Forests
- MapReduce
- Spark
- Bayes Theorem
- Bayesian Methods
- Text Data
- Clustering
- Deep Networks
4. Python for Data Science and Machine Learning Bootcamp — Udemy (Beginner)
This course is extremely well planned and well explained. The instructor explains the concepts and the assignments are a wonderful addition for those who believe in the saying “Practice makes a man perfect”.
Skills You Will Gain:
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter notebook
- Seaborn
- Pandas Built-in Data Visualization
- Plotly
- Cufflinks
- Geographical Plotting
- NLP
- Deep Learning
- Neural network
- Big DataSpark
5. Data Science MicroMasters — UC San Diego @ edX (Advance)
This course is aimed at people who are already comfortable with basic Python concepts. The prerequisites for this course are higher than the others in this list as it is equivalent to a graduate-level course that counts towards a real Masters at several institutions. This is a well-balanced, extremely comprehensible course for people looking to add to their knowledge and skill pool.
Skills You Will Gain:
- Python
- Probability and Statistics in Data Science
- Spark
- Machine Learning fundamentals
We hope that our blog helps you find your way towards your goal and helps you to become a Data Scientist.
On a final note…
Living, breathing, and eating data is the new motto of this century, and Data Science is a magnificent field exploring the data. This field is alluring as it is adventurous and lucrative. We will always encourage enthusiasts to pursue their dreams to become a Data Scientist. But before that, it is necessary to know the fundamental differences between similar fields like Data Analysis. It is also essential to be aware of the required tools and technologies needed in this field.
The key to becoming a successful Data Scientist is keeping yourself updated and connected. In addition to the given recommendations, we would suggest you join a community to expand your reach and understanding. Meetups and Seminars are a great way to increase your networks and learn from your peers.
We know that pursuing dreams is an extraordinary journey, and we hope that with this blog we have equipped you with sufficient knowledge for you to embark on this journey.