We live in a world that’s run by data. Whether we’re aware of it or not, we produce, consume and process data every second of every day.
With all of the advancements in technology, we now have the capability to harness and utilize data as we’ve never had before.
The dawn of the Age of Information has given rise to concepts like Data Science and Big Data, which are culminations of different disciplines that were combined to perform specialized functions.
The ability to manipulate and analyze data to produce value has rapidly become one of the most in-demand skills in a number of industries.
From healthcare to finance, the use of Big Data has become almost essential to push industries and best practices forward.
This demand has opened the doors for the emergence of professions like Data Scientist, Data Engineer, and Big Data Analyst.
But, before we discuss how to transition into these specializations, we first need to define its cornerstone.
What is Big Data and how to use it?
Put simply, Big Data is unstructured data with a size that goes beyond the limits of what commonly used software tools can manage.
Big Data itself isn’t an entirely new concept. It’s been around since the 90s and early 2000s but has gained more traction recently because of the upsurge of new computing techniques.
Three V’s — volume, velocity, and variety — have always been tied to its core concept, but four additional V’s — veracity, variability, visualization, and value — to round out its definition.
There are many factors involved in dealing with Big Data, with each having an equally prominent role leading up to its effective interpretation.
Social media sites, web forms, and wearable technology are some of the examples of channels that are used to capture data.
Data warehouses are then used to store all of the accumulated data. They would need to be robust and scalable to accommodate the sheer amount of incoming input.
Since Big Data is commonly raw, data wrangling techniques are employed to make the data more manageable.
Once everything has been sanitized, data manipulation techniques are utilized to make sense of all the information and transform them into actionable insights.
That’s where Big Data Analysts come in.
What does it take to become a Big Data Analyst?
The path to Big Data mastery can sometimes feel long and arduous, but the end result could ultimately be very rewarding.
Techniques and tools are constantly changing, and so should the knowledge of aspiring Big Data Analysts. The best laptop for data science would certainly be handy as well!
With a plethora of readily available resources, it can become overwhelming to start learning. Sometimes, it’s better to have a little help along the way.
Online courses and certifications would definitely give you a boost. Taking a big data course will help keep you up-to-date on the latest trends in the industry.
But, before you start to dive in, here’s a list of skills you would need to strengthen to get you up and running in your journey to learning Big Data:
1. Software Programming and Knowledge of Frameworks
Nowadays, software programming has become an essential skill in many industries. Even basic knowledge of programming would go a long way.
Big Data facets like data wrangling and data manipulation rely heavily on customization of the code involved, so programming is almost always a hard prerequisite.
If the need arises, this skill would also be useful in terms of capturing and gathering data from several sources.
Knowledge in programming languages like C++, Ruby, Matlab, Julia, Scala, and Weka are a big plus, but knowledge in R, Python and Java are enough to get you started.
Apart from programming languages, you would also need to use pre-existing frameworks to process information.
Hadoop, MapReduce, Pig, Kafka, Spark, and Storm are some of the most prominent frameworks commonly used in Data Science and Big Data.
Programming skills are also essential in maintaining and managing data warehousing systems. Relational and non-relational database systems are both used, so you would need to be familiar with both.
MySQL, PostgreSQL, Teradata, and DB2 are the traditional databases that enterprise applications use, while NoSQL approaches like MongoDB, Cassandra and HDFS are gaining popularity.
It might be intimidating for people with non-technical backgrounds to get into it, but learning how to program provides several long-term advantages in terms of career growth.
2. Statistical Knowledge and Critical Thinking
Knowledge of Statistics and linear algebra is fundamental to any trade involving data analysis. It’s an understated skill but one that could prove very useful in terms of making sense of the data.
Being able to process data is one thing, but being able to spot logical connections hidden between the information is one of the most essential parts of data analysis.
Sharpen your problem-solving skills and logical reasoning to help strengthen your inferential acuity.
By using tried and true mathematical techniques combined with critical thinking, you would be able to effectively provide useful actionable insights that could help drive decision-making.
3. Communication Skills
Last, but certainly not least, even though you have cleaned the data and gained insights, the information won’t be effective unless you can communicate it clearly.
Read up on useful articles to improve the way you convey the insights you have gathered. See how others present information and learn from how others are doing it. Practice writing every single day so that you can get a feel of how to better organize the flow of your presentations or write-ups.
Information is only useful when the people at the receiving end would be able to fully understand what they’re consuming. Make it a point to sharpen this skill every chance you get.
You’re all set!
To quote Laozi, “A journey of a thousand miles begins with a single step.” If everything seems like it’s too much to process, take small steps until you get to where you need to be.
Start by reading useful articles or enroll in a self-paced online course. Any action is better than waiting around for the information to come.
The world is full of a nigh-infinite amount of data, and it’s not going to just process itself.