How to become a Data Scientist?

Data Science is providing tremendous business value such as precision in forecasting and gaining insights on what can be beneficial for the customer. Take for instance streaming sites like YouTube etc. Data science is being used to mine data about what kind of videos attract users’ interests. This helps the site to recommend similar videos based on their specific choices and interests.

5 skills every Data Scientist should have

In today’s world, where the competition is immense, data scientists are more in demand than ever. If you are interested in entering this field and want a list of skills that you need to master then you are at the right place. We have listed 5 essential skills you need to master in order to be a good Data Scientist.

1. High-level understanding of Python, Hadoop, and SQL

As a data scientist, it is essential for you to be good at programming languages like Python, Hadoop, and SQL. The data provided to a data scientist is usually in form of large data sets and being able to understand and decode it requires good programming knowledge.

Only after you have understood the dataset can you mine data and identify peculiar characteristics and patterns. The work of a data scientist is more applied than theoretical.

2. Strong Decision-Making Prowess

A data scientist should have good intuition and decision making skills to identify the product and have a concrete knowledge of the complex system and data. When a data scientist has a good product intuition, he can generate general hypothesis on how he can improve the product, and get good insights.

It is also necessary for a data scientist to know how to define metrics of the product so that he knows what he can do to make it better.

3. Ability to work well in teams

Data science is not an easy job, and it is definitely not something one can do alone. A data scientist needs to have good teamwork spirit if at all he wants to do his job as per expected outcomes. Be it ETL specialists or data analysts, or even stakeholders form the business side, a data scientist will have to collaborate with many teams from time to time. Having a good team spirit makes workflow smoother.

4. Good Communication Skills

Even if a person is good in data science, and has obtained excellent insights, it is essential for him to know how to communicate his findings in a perfect manner. All the insights obtained from such deep-end and thorough research will be of no use if it is not communicated effectively. Different business units must be able to derive the much-needed data to make better business decisions.

5. Excellent Quantitative Analysis

A data scientist must mine data and identify peculiar characteristics and behavior from the given data. Hence, quantitative analysis becomes an essential skill for a data scientist as the datasets provided are very large. In order to efficiently identify products and their behavioral characteristics, a data scientist needs to have quantitative analysis as his expert skill.

Thus, if you want to become the most sought-after data scientist in the industry, then these are the five key skills you must possess to be good at your job.

How to start your career in Data Science

Data science is known to power up business value across industries like financial, healthcare, and technology. Professionals in this field empower the management to make better data-backed decisions and help unlock opportunities previously hidden. If you want to start as a data science professional then this field is extremely lucrative for you. However this move can reap rewards only when you approach your career in a strategic manner.

Interested to know of some handy tips when you are starting a career in data science? Then this post will help you

1. Examine the multiple roles in data science

The first thing you need to do is examine the roles that are available in the data science sector. Some of the key roles include machine learning specialist, data engineer, data visualization specialist and others.

Choosing the correct specialty is extremely important. You need to match your role with the work experience and the background that you have in your career. Hence, it would be wise to gather information regarding each and every role of the industry and match your qualifications and expertise to select the best one for yourself.

2. Obtain skills for the role

The next thing you should do is to start looking for the educational courses that can help you prepare for the role you have selected. For that, you need to take a course that suits your interest. The theories can help you learn the skills and prepare for the challenges of the career. But make sure you are selecting a reliable course to obtain the skills.

3. Get in touch with industry specialists

Surrounding yourself with experienced professionals is a great move to keep yourself motivated and grow continuously. Though it can seem a little difficult at the beginning, you will eventually start interacting with more and more specialized people.

You can start by interacting with the specialists on social media and indulge in a valuable technical discussion in your niche.

4. Merge theory with a practical approach

Data science is not only about technical theories. The practical approach is extremely important if you are looking for a long term run in the industry. You need to grow your practical knowledge of the work that is required in the industry. For that, you need to spend more time in practical applications through different working opportunities.

5. Work on your communication capabilities

Just because you are in a technical field, doesn’t mean that you should stop yourself from obtaining better communication capacity. The data science industry offers an immense level of positive career growth to the professionals with strong communication skills.

6. Gather resources for current trends

To stay ahead in the game, you need to present yourself as a modern and well updated professional. Only then, you can expect companies to put their faith in you. So, keep on gathering resources regarding the current trends. Also, join a peer group online where you can share knowledge about data science tools and resources – Reddit is a great place to start with. Also, attend webinars and online sessions that help you stay updated on the current trends.

7. Choose the right tool or language

Choosing the right tool or language could be a difficult decision, but we suggest that you use the widely used tool / language to begin with. In this way, it will also be easy to find resources and tutorials online. It is imperative that you understand the concept rather than focusing just the right tool. Start with the coding language that you are familiar with and slowly begin to build on it. If you are new to coding then you should prefer GUI based tools to get started off with.

Challenges will come your way. But amidst all this, it is your motivated approach that will help you in conquering all those challenges. So, keep on trying and making your way towards desired success. If you are an upcoming data science professional then these handy tips will prove to be vital in giving you a good foundation to a successful data science career.

The Architecture Of Data

Data is a critical aspect of every single business. Handling it becomes even more critical. Unless you have set protocols to handle and assimilate your data to be utilized wisely, your business can suffer in the long run. A stringent architecture of your data platform can save you a lot of future hassles.

Today, we try to understand the basic setup of such data platforms.

The main components of a data management platform are as below:

The Data Collection Layer

The data collection layer is divided into 2 parts:

Client-side – the part is responsible for collecting the data and sending it to the server-side data collector. There are a number of ways this could be done, for example with a JavaScript tracker, an SDK, or other libraries.

A JavaScript tracker and impression pixel may also set off piggyback pixels to sync cookies with third-party platforms.

Server-side – provides the endpoints responsible for:

• Receiving the data from the client-side libraries – typically, very lightweight and just used for logging the data or pushing them to the queue(s) for the next layer to process.
• Syncing cookies with third-party platforms and building cookie matching tables that are used later during the audience export stage (see below).

The Data Normalization and Enrichment Layer

Once the data has been captured from the data collection endpoint, the DMP normalizes and enriches the data.

The data normalization and enrichment process can include a number of the following actions:

• Deleting redundant or useless data.
• Transforming the source’s data schema to the DMP’s data schema.
• Enriching the data with additional data points, such as geolocation and OS/browser attributes.

The Data Storage, Merging, and Profile Building Layer

The next step is to store and merge the newly collected data with existing data and create user profiles.

Profile building is an essential part of the whole data-collection process, as it is responsible for transforming the collected data into events and profiles, which are the cornerstones of audience segmentation (the next stage).

A user profile could contain several identifiers, such as cookies or device identifiers, as well as persistent identifiers that are pseudo-anonymized – e.g. hashed usernames or email addresses.

Another important part of the profile-building stage is the matching of data sets using common identifiers — e.g. matching an email address from a CRM system with an email address from a marketing-automation platform.

A profile consists of user attributes (e.g. home location, age group, gender, etc.) as well as events (e.g. page view, form filled in, transaction, etc.). The latter is typically a separate collection or table in the database.

The Data Analysis and Segmentation Layer

The core functionality of a DMP is analyzing the data and creating segments (e.g. audiences).

An audience segment is useful to advertisers and marketers (and publishers) because it allows them to cut through the mass of data available to them and break it down into digestible pieces of information about potential customers, site visitors or app users.

With good audience segmentation, advertisers can buy display ads targeted at a group of Internet users and publishers can analyze site visitors and then sell inventory at a higher price to media buyers whose target segments match the publisher’s.

Audience Export

Audience export is a component that periodically exports segments to third-party platforms, for example demand-side platforms (DSPs), in order to allow advertisers to use them in campaign targeting.

User Interface

This is pretty self-explanatory – you need to give the users a UI to create segments, configure data sources, analyze and visualize the data, as well as provide them with the ability to configure the audience exports to third-party platforms.

Application Programming Interfaces (APIs)

APIs can be divided into the following categories:

• Platform API used to create, modify, and delete objects such as users, segments etc. – basically for whatever task the user is able to do via the UI in the platform.
• Reporting API used to run reports on the data. Due to the sheer amount of data, some of the reports may need to be scheduled for offline processing and made available for download once generated.
• Audience API that allows client libraries to query in real-time whether a given visitor belongs to the audience or not.
• Data ingestion API used for importing the segments or other data from third-party platforms. Again, as the data volume may be large, this can happen through an Amazon S3 bucket or file upload that is queued by your DMP for offline processing.

This, of course this a simplified example and the actual components and architecture may get more complex as you add additional features and integrations.

Learning Big Data The Right Way

Who should learn Hadoop? – Anybody with basic programming knowledge can learn Hadoop. Mostly professionals from Business Intelligence (BI), SAP, Data Warehouse, ETL, Mainframe background or any other technology domain can start learning Big Data with Hadoop.

When we are discussing the prerequisites for Hadoop, we need to understand that Hadoop is a tool and it does not have any strict perquisites or requirements before because of this only it is the most powerful and useful tool in today’s data world. We need to understand why Hadoop is impacting so much because it is not fixed or restricted in a particular domain.

There is no strict prerequisite to start learning Hadoop. However, if you do want to become an expert and make an excellent career you should at least have a basic knowledge of JAVA & Linux. Don’t have any knowledge of Java & Linux? No worry. You can still learn Hadoop. The Best way would be to also learn Java & Linux parallel. There is an added advantage of learning Java and Linux that we will explain in following points

• There are some advance feature that are only available in Java API.
• It will be beneficial to know Java if you want to go deep into Hadoop & want to learn more about the functionality of particular module.
• Having a solid understanding of Linux Shell will help you understand the HDFS command line. Besides Hadoop was originally built on Linux & it is preferred OS for running Hadoop

There is no strict prerequisite to start learning Hadoop. However, if you want to become an expert in Hadoop and make an excellent career, you should have at least basic knowledge of Java and Linux

To completely understand and become proficient in Hadoop there will be some basic requirements to which developer needs to be familiar with. Familiarity with Linux Systems is a must. Most people lack this ability.

For Hadoop, it depends on which part of the stack you’re talking about.  For sure, you’ll need to know how to use the GNU/Linux Operating System.  We would also highly recommend programming knowledge and proficiency in Java, Scala, or Python.  Things like Storm give you multiple languages to work with.  Things like Spark lend itself to Scala.  Most components are written in Java so there’s a strong bias to having good Java skills.

“Big Data” is not a thing, but rather descriptive of a data management problem involving the 3 V’s.  Big data isn’t something you learn, it’s a problem you have.”

More and more organizations will be adopting Hadoop and other big data stores which will rapidly introduce new, innovative Hadoop solutions. For this, Businesses will hire more big data analytics to provide a better service to their customers and keep their competitive edge. This will open up capabilities for coders and data scientists that will be mind-blowing. – “Jeff Catlin, CEO, Lexalytics”.

So, we recommend the following to kick-start your career in Hadoop. 

• Linux Commands – for HDFS [Hadoop Distribution File System]
• Java – For Map Reduce
• SQL – For Databases
• Python to write codes.

Go big with big data!

Big Data Is Big

Huge information will turn into a key premise of rivalry, supporting new influxes of efficiency development, advancement, and shopper overflow—as long as the correct strategies and empowering agents are set up.

This has what has happened in the last few years in our world.

Data became Big

As everyone pointed out that with ubiquity of internet and internet connected devices, buttload of data gets generated. This is going to become astronomical in coming future as more and more sensors, people, and devices gets connected.

Now you have data. You can do quite a few things with large data to increase revenue, make service/product better, make forecast more accurately, convince investors/acquirers with facts, make and provide input to critical decision making. But to do all this you need data scientists.

Data became Open

Data is now more available than ever. If you ever wanted to see if your company’s name is referred by people with positive sentiment or negative without actually people filling the Google form or SurveyMonkey forms, you can always stream in Twitter data and do simple Natural Language Processing using Python (programming language)’s Natural Language Toolkit (NLTK). You will need a data scientist for this.

Twitter  is not the only open data source. There are valuable data on AWS & Public Data Sets. If you are a startup focused on Genomics, you’d probably prove that your flagship product works on 1000 Genomes Project.

Right Tools became Accessible

It seems a need to analyse data sets, usually large, leads into high demand of Data Scientists. But there are a couple of factors too. The accommodation of large set used to be hard. MySQL or traditional datastores have their limits. You tune them carefully, and be very careful what not to do to keep the database performant. With availability of robust tools like NoSQL databases and distributed computing, the general approach has become to throw everything in your NoSQL cluster and we may or may not use it to analyze some statistics.

The second half of the story is open sourced, big data processing technologies. They do the hard job of crunching number. They are faithfully used by the big companies and they are free.

Success Stories became Cliché

If you look for Big Data success stories, you will find many many companies used data science (analytics) to increase revenue, improve user base, increased user engagement (YouTube), innovated an existing process or simply raked dollars by providing big data analytics as service.

Hardware became cheap

Imagine 10 years back (2004). You have same amount of large data as today. Same amount of storage technology and processing power from software as today. Could you just bought 42 units of Dell PowerEdge R910 4U rack server on day 0 for some analytics that may or may not help you to improve 1% in revenue? No. But now you can just rent a couple hundred machine instances from any cloud service provider for an hour, do the analysis. Kill the servers. Your job is done in a couple hundred dollars. Compare that with seven thousands dollar for a single Dell machine.

So, enabling technology with cheap hardware availability caused many companies to try out data analytics for maximum gain from their business. So, many people hire data scientists to do that.

Basically, in today’s age, the following has happened:

Data Became Big: That means lot of data sources

Data Became Open: Twitter, government, open data and many more.

Right Tools became Accessible: Open Source reliable tools: Hadoop, NoSQL, Storm

Success Stories became cliche: Success stories, and high paying jobs.

Hardware became cheap: The cloud computing movement.

Future became data driven: With push from pro-data scientists, it seems data is the future.

And that is why Data and everything revolving around it is the next big thing.

Apache Kafka – A Scalable Messaging System

Kafka is a distributed messaging system that allows to publish-subscribe messages in a data pipeline. It is a fast and highly scalable messaging system and is most commonly used as a central messaging system and cnetralizes communication between different and large data systems.

Image reference: http://kafka.apache.org/documentation.html

Advantages of using Apache Kafka

1.) Highly Scalable:

As mentioned earlier, one of the major advantages of using Kafka is that it is highly scalable. In times of any node failure, Kafka allows for quick and automatic recovery. In a world that now deals with high volumes of real-time data, this feature makes Kafka a hands down choice for data communication and integration.

2.) Reliable and Fault – Tolerant:

Kafka helps to replicate data and also supports multiple subscribers. Thus, in case of any failure there is no fear of data crash. Kafka is a fault-tolerant messaging system,  thus making it a highly reliable pub-sub messaging system among the many others

3.) High Performance:

Kafka is super efficient at handling real-time and complex data feeds with high throughput and lesser delays. The data and stored messages can run into terabytes, yet Kafka delivers high performance and the best companion for any enterprise Hadoop infrastructure.

Popular use case scenarios for Apcahe Kafka

1.) Messaging

A message broker is used for many reasons such as separating data lines from data producers, buffer and load unprocessed images etc and Kafka works as the best messaging broker to support all these activities. Also, with the credibility of being fault-tolerant and highly scalable, Kafka is a good solution for processing large scale messages.

2.) Website Activity Tracking

The main use of Kafka was to help to track and analyze real-time feeds of complete website activity such as page views, search, publish and subscribe and any activity that user performs on the site. All these activities are stored as separate topics in the data pipeline.

Kafka is also used to track high volumes of data activities as each page view can generate multiple messages.

3.) Log Aggregation

Kafka helps to collect distributed data files and puts them all together in a central place for processing. It de-clutters the extra details and only gives log and event data that has been recorded. Kafka is better suited from other log-centric systems because of greater performance and durability due to data replication.

4.) Stream Processing

Kafka helps to process data in multiple stages where the raw input data procured from Kafka topics is aggregated, enriched and transformed into new topics for further data mining.

Right from crawling content, to publishing it and further categorizing it under relevant topic and then attempting to recommend the content to users, Kafka does it all! The processing pipeline is quick and has low latency. It also provides real-time data graphs and hence is considered to be the most reliable stream processing tool.

Thus, Kafka is an amazing big data processing tool that most MNCs such as LinkedIn, Twitter, Pinterest and many more use as their publish-subscribe messaging system and also to track data. Its durability and scalability give Kafka an edge over other big data solutions.

Big Data in Business Today

With 2.5 quintillion of data produced every day, Big Data is gaining tremendous traction in analyzing this humongous volume of data from disparate structured and unstructured sources. It has proven to drive enhanced business performance with the help of real time analytics and enable smart decision making backed by strong data analysis.

Here are some examples of how our daily lives are changed for the better with the help of Big Data applications:

1 – Big Data in healthcare

As healthcare costs keeps rising, Big Data and related technologies are stepping in for more efficient patient care. It can reduce clinic visits with the help of vitals monitoring devices fitted on the patient. These monitors relay information such as heart rate or BP level, so that the physician can take real time action to prevent escalation of deteriorating health condition. This remote physician care can be administered even if the physician is located miles away from the patient.

2 – Big Data in e-commerce

An online shopper’s entire journey from visit to sale is tracked by Big Data to provide insightful information about buying behavior, preferences, and socio-economic demographic profiling. This helps in segmenting the shoppers into appropriate groups so that deeply customized and targeted marketing messages can be relayed to the appropriate group at the right time.

Big Data also tracks purchases made 3 to 4 months prior to a big ticket event like Black Friday or Big Billion Day Sale. This helps in a better understanding of what products need to be stocked and thus ensure proper inventory management.

3 – Big Data in Navigation Assistance

Our Maps services are enabled by GPS. This in turn is made more precise with the help of tons of reports fed into the systems so that the coordinates are made as accurate as possible. Big Data relies on diverse sources of information such as individual data from apps, incident reports, or road traffic reports for value added services like depicting the shortest route or the fastest route from the source to destination.

4 – Big Data in Entertainment

See recommendations in music based on your music playing preferences? This is nothing but Big Data at play. Apps like Pandora and Spotify have mastered the use of Big Data to generate personalized recommendations to music lovers based on their listening preferences. Spotify’s work in this sector is particularly interesting. It has acquired The Echo Nest to power up their music recommendation engine algorithm. The algorithm isn’t limited to simply analyzing and classifying music. It also uses web crawling to obtain information about the artist, the song, or the music label and includes this insight into the analytics engine.

To conclude

These real life use cases aptly denote the extent to which Big Data has entered our lives. From healthcare to education, there is no sector is left untouched by Big Data. As a smart business owner, it will be unwise to ignore these trends.

The Big Data Industry

Most experts believe that Big Data is proving to be quite a Game Changer. The majority of the modern industries have started implementing it to their advantage, while some old school industries have started warming up to it. The advantages of Big Data are numerous; increase sales and cost cutting are some of them. To know more about which industries are best utilizing Big Data, read the lines below.

1.     Financial Markets

When data is one of your industry assets, it pays to invest in it. Financial data analytics has resulted in big payoffs for the companies implementing it. When real time data is of utmost importance it pays to invest in them. Hedge fund managers have been using Big Data Analytics for long in the form of cutting edge Data Science.

2.     Retail Banking

From the last 2 decades, retailers have started analyzing consumer buying behavior and data. They need to know the demand and supply equation, as retail is a season centric business. As thousands of pieces of data are collected in a given year, Big Data will help the information become more accurate. This, in turn, will help retailers accurately forecast demand and thereby buying that requirement of stock. The company will not only save on wastages, but it will also help to satisfy customer need as they won’t have to return due to lack of stock.

3.     Energy

Today developed countries have started using Big Data to balance loads, manage infrastructure and price energy products. The Alternate energy consortium has taken a lead regarding Big Data. The old school energy industries are slowly catching up to this trend. Smart meters are proving to a boon in this space, as it provides millions of pieces of data to the supplier and the regulator.

4.      Web Analytics

There are tremendous opportunities for Big Data in the web analytics field. With the popularity of social media, humungous amounts of data packets are generated. With Big Data we can know a user’s profile, how many times he logs in a day, where he has shifted his house. Also, you can find the user’s spending habits as well as the amounts of time he spends on a particular website.

5.     Mining and Natural Resources

Most mines are fully automated with sophisticated machines and Equipment. They throw up large amounts of data daily. Sometimes the size of a single day data is in terabytes. This Data can be suitably used to increase mine efficiency. It can also channelize to improve yield optimization and predictive asset management. The value chain in the mining industry can be further strengthened with the help of Big Data.

With this wide coverage, it is no wonder that ‘leaders’ within 900 organizations surveyed by IBM are 166% likely to make most decisions based on data. Big Data analytics will drive strategy and tactics around the data explosion that will continue to happen as we read this post.

Mind Blowing Facts About Big Data

We have been seeing a lot of hype surrounding big data but we believe Big Data paints a much bigger picture than what we perceive and the following facts will help to paint a realistic picture of the phenomenon, a phenomenon that is changing the world as we know it.  

1. Every 2 days we create as much information as we did from the beginning of time until 2003 .

2. Over 90% of all the data in the world was created in the past 4 years.

3. It is expected that by 2020 the amount of digital information in existence will have grown from 3.2 zettabytes today to 40 zetabytes.

4. The total amount of data being captured and stored by industry doubles every 1.2 years .

5. Every minute we send 204 million emails, generate 1,8 million Facebook likes, send 278 thousand Tweets, and up-load 200 thousand photos to Facebook .

6. Google alone processes on average over 40 thousand search queries per second, making it over 3.5 billion in a single day.

7. Around 100 hours of video are uploaded to YouTube every minute and it would take you around 15 years to watch every video uploaded by users in one day.

8. Facebook users share 30 billion pieces of content between them every day.

9. If you burned all of the data created in just one day onto DVDs, you could stack them on top of each other and reach the moon – twice.

10. AT&T is thought to hold the world’s largest volume of data in one unique database – its phone records database is 312 terabytes in size, and contains almost 2 trillion rows.

11. 570 new websites spring into existence every minute of every day.

12. The boom of the Internet of Things will mean that the amount of devices that connect to the Internet will rise from about 13 billion today to 50 billion by 2020.

13. Big data has been used to predict crimes before they happen – a “predictive policing” trial in California was able to identify areas where crime will occur three times more accurately than existing methods of forecasting.

Big Data is Big, literally and figuratively. Its implementation can save your business huge losses and lead your firm towards a data friendly future.