As per the recent LinkedIn survey, Data Scientist, Big Data Developer & Data Analytics are the most searched technologies in last 2 years. Read the blog till end to understand which profile suits you best.
First, How to Become a Data Scientist?
- Learn Python/R
- Learn Machine Learning
- Learn Deep Learning
- Learn Artificial Intelligence
Python/R languages is used for Data Science & we can use both Python/R to solve any Machine Learning problem and to perform predictive and statistical analytics. Both Python & R provide very rich libraries related to Data Analytics.
Most of you still have a query after learning Python, R programming can someone become a Data Scientist?
Answer is No, Python/R programming language can help you to code statistical models but first you need to learn to stats and various algorithms. To Become Data Scientist, you need to master:
- Statistical Analysis
- Machine Learning Models, Clustering, Regression, Classification, Natural Language Processing Algorithms, Deep Learning, Artificial Neural Networks, Tensor Flow
- Then, learn any programming language like Python/ R programming
- Implement a real time project like Chat bot, Sentimental Analysis, etc. which will actually make you job ready.
Statistical Analysis is based on collecting and scrutinizing data samples from which insights can be drawn. The objective of statistical analysis is to identify patterns from the data, example:
Statistical Analysis on Agricultural data sets:
- Collect the crops data from trusted government sites
- Create sample data using probability distribution
- Analyze the data using statistical methods
- Create a model to understand how sample data relates to the performed analytics
- Prove the validity of model
- Perform predictive analytics using Artificial Neural Networks
- Perform graph visualization, state wise crop generation
This way we can validate suitable statistical model for our data set giving the best results. Next step is to make computers understand this model and give appropriate results on tons of data. Simple and best way of doing this is using Machine Learning.
It is the primary source of artificial intelligence. In short, we can state that it induces computers to enter into self-learning mode with no explicit programming.
- Once we fed new data into the core chips of computers, it will start to learn, grow, change and to develop by themselves.
- With a short period of time, machine learning becomes much popular and has been around for some times now. Nevertheless, the ability of computers to apply mathematic calculations automatically is gaining a bit of momentum.
- Machine learning is everywhere from social media to self-driving Google Car. We will be seeing such great techniques in Online recommendation engines, while using friend recommendations on Facebook, cyber fraud detection and Offer recommendations from Amazon.
This powerful technique helps analyze data and easing the task of being a data scientist. The automatic process that these techniques used to make this machine learning field constantly evolving.
Artificial Models & Deep Learning Models are artificially intelligent means they are automatically trained on real time data sets. Speech recognition and face detection are few examples of AI applications.
- Develop and Implement various Deep Learning Algorithms in daily practices & Live Environment.
- Build Real time Deep Learning Applications
- Implement Data Analytics models (CNN & RNN) on various Data Sets
- Data Mining across various file formats using Deep Learning models
- Building Image & Video Classifiers, Speech Analytics using Deep Learning models
- Perform various type of Analysis (Time Series, Image, Video, Audio, Face Detection & Recognition)
- Implement plotting & graphs using various Deep Learning Libraries (Tensor Flow & Keras)
- Perform Big Data Analytics using DeepLearning4j & other frameworks
- Building different Neural networks using TensorFlow, Keras, PyTorch & other Deep Learning Libraries.
Second, How to Become a Big Data Developer?
- Learn Hadoop & Spark
Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.
However, companies expect a lot of skills from one person. Knowing Spark with Hadoop increases lot of good opportunities and make you Job Ready.
As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That’s not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.
Apache Spark is becoming the most popular big data technology worldwide. It is a big data computation framework just like Hadoop. The only difference is that Spark is faster than Hadoop. This is because Hadoop reads and writes to disk, which makes it slower, but Spark caches its computations in memory.
Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time. It also helps data scientist to handle complex unstructured data sets. You can use it on one machine or cluster of machines.
Apache spark makes it possible for data scientists to prevent loss of data in data science. The strength of Apache Spark lies in its speed and platform which makes it easy to carry out data science projects. With Apache spark, you can carry out analytics from data intake to distributing computing.
Third, How Become a Data Analyst ?
- Master Tableau/ Power BI
Tableau/ Power BI:
The business world produces a vast amount of data frequently. This data needs to be translated into a format that will be easy to comprehend. People naturally understand pictures in forms of charts and graphs more than raw data. An idiom says “A picture is worth a thousand words”.
As a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, Tableau, and Power BI.
- These tools will help you to convert complex results from your projects to a format that will be easy to comprehend. The thing is, a lot of people do not understand serial correlation or p values. You need to show them visually what those terms represent in your results.
- Data visualization gives organizations the opportunity to work with data directly. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of competitions.
- With Tableau, Power BI we can easily connect our servers to various data sources from files and web servers. In this way, you can easily work on various file formats such as JSON, TXT, CSV, etc.. Moreover, you can get your file data imported easily from web servers like MYSQL, Tableau Server and Amazon Redshift, etc.
Power BI/ Tableau reports look like:
As the world of data analytics and visualization is moving so fast, new brands are keep coming to every nook and corner of the world. To be updated in the thread, we should use the tool that comes with the blend of ease of use, price and brand recognition. Although both are available at reasonable cost, people prefer Power BI over Tableau.
Tableau seems to be costlier than Power BI, however, we can use the free version of this tool yet it comes with limited capabilities. Unlike Power BI, Tableau comes with special features that let you visualize things in an enormous way but much of their advertising efforts are focused on bigger budgets and with data engineers. The more you pay for the very first time, the more it lets you access this tool. While you are using the paid version, you can easily access the benchmarked data from some third parties. Moreover, it has major versions and non-profit tool for some specific academic settings.
SQL represents structured query language, which is designed in such a way that it communicates with the database. As per the American National Institute, it is a primary and standard language for relational database Management software.
- It doesn’t require an expert to learn and understand this language as it seems to be simple.
- Some of the standard SQL commands are Insert, update, create, delete, drop, etc. While most people using SQL as their database system, some uses additional proprietary extensions.
- Some of the popular relational database management systems that SQL uses are Microsoft SQL server, Ingress, Access.
Although Excel is just a data keeping tool, it can do a lot for your basic daily kinds of stuff.
- The advanced Excel is an upgraded version of Excel, which helps users to analyze, crunch and get answers from any complex questions to be solved.
- While a beginner can use IF formula or simple formula, an advanced user can write and combine complex formulas, which advanced user can do multiple things with Advanced Excel software.
- We can combine formulas like INDEX, SUMIFS, MATCH, SUMPRODUCT, LOOKUP, and other such formulas.
Macro code is a simple programming code which is designed in Visual Basic for some Applications language. The macro code is designed in such a way that it can automate data entry and calculation kinds of stuff that you do manually there. For instance, you can use this great code to print any specific cells with a single click by just selecting the range-> File Tab -> Print -> Print Select -> OK Button.
MIS means Management Information system which comes with the blend of hardware, software and people to store, collect and process data to produce relevant information that most decision makes can use to their daily routine in their professional life.
- Entrepreneurs and decision makers hardly need this MIS system to process their data properly to tackle their daily things.
- Moreover, this system let any employees in an organization access the Short message service and Email to communicate properly within the Management Information system that the company is using.
- On top of that, it helps in keeping valuable records such as managing the business transaction and other such confidential details, etc…