Category: Data Analytics

Categories
Career Data Analytics

Climbing the Data Ladder: Career Paths and Skills for Aspiring Data Analysts

Aspiring to be a data analyst? It can be one of the most promising decisions in terms of boosting your career. A data analyst is necessary in multiple domains of the industry. Therefore, picking data analysis as a field will offer you a variety of career options. For a field so vast, it also becomes possible to go astray and be misled by wrong information. That is why, it is just as important to gather information from a rather credible source, and in a data-oriented field, as is, it becomes even more imperative to filter the data coming to you. To help you with the same, a list of career options has been given below:

1. Analytics Specialist

If you get into a field, do it well, well enough that you become a specialist in the same. With the need for data analytics on the rise, it can be a lucrative career opportunity. The position involves working with complex data modelling and predictive analytics. The career path asks for advanced analytical skills from you, out of which data visualisation and statistical programming are also a part.

2. Machine Learning Expert

Machine Learning has shown remarkable growth in recent times, making it a rather alluring area of work. As a Machine learning expert, you work by interpreting complex data sets to achieve goals set by the organization. Seeing the growth this field has shown, Machine Learning and Data Science have grown to be one of the fast-growing career paths.

3. Data Visualisation Expert

It is not just about the data you are working on, but the ability to present it as well. For this, data visualisation becomes important, to represent the data in a format easily understood by the target audience. Data visualisation takes the help of certain dashboards, and reports, such that it is easily interpreted by non-technical individuals.

4. Environmental Data Analyst

With rising concerns about the environment, it is even more crucial to care and analyse the same. Therefore, times like these call for new opportunities popping up as an environmental data analyst, the one tasked with the responsibility of assessing environmental impacts and sustainability.

5. Social Media Data Analyst

Social media is trending these days, be it one platform or the other. With millions of users worldwide, and different patterns of usage, everyone’s usage, and view about the same is unique. To understand it better, and gather insights from the same, there is a need for social media data analysis. They analyse public opinion and preferences, and draw insights. To boost an organisation’s social media presence, to keep keeping up with the trends, they are behind it all.

6. Financial Data Analyst

Running a big large organisation, and lacking an adept team of financial data analysts doesn’t seem to be a trustworthy combination. With capital-intensive companies, it is important to put together a team of analysts to analyse, assess, and improve business and investment decisions. This is where financial analyst comes in, with their expertise in financial modelling and data interpretation, the profitability and improvement in financial strategy can be observed. For this reason, it is also considered to be one of the most crucial career paths, still not known to a lot of people.

Categories
Data Analytics

What all analytical skills should a product manager possess?

Eyeing a juicy career as a product manager, there are certain things you need to be aware of, and that also includes being proficient enough to lead your team. For the perfect product manager, tons of skills should be in their arsenal, but a certain few are a must if we are talking about some dire necessary product manager skills. To make the task easier, pulling out all the data about the same, the insight about each skill, and the use case of each has been offered in the form of the list attached below:

  1. Communication skills: This is the stepping stone that you need to cover if you are entering the ruthless corporate world. Good communication is needed in every phase of your job, from product meetings to presentations, or effectively working with your team. A product manager needs to marshal his team and provide them with on-point guidance and support, for which communication is an utmost necessity.
  2. Business Intelligence: Business intelligence comes in at a crucial level when working with multiple projects, and diverse ones. A product manager needs to be well-versed to understand the model well, pitch it to the seniors and talk in depth regarding the same with the stakeholders.
  3. Technical Expertise: This is one skill that a product manager cannot miss out on. If you are rallying a team, it is important to be well-equipped with the minor technicalities, and that also includes technical know-how. Working on a project, technical expertise comes in handy when outlining the project requirements, and bottom-line working of the system.
  4. Research skills: You do not just build information, you dig it out of places scattered all over. Therefore, the skill of research comes in handy when working on a rather niche project, where you will need a wide range of information, and you have to look up and analyse multiple sources of information to look for that crucial piece that might work out for you.
  5. Analytical Skills: Going hand in hand with research skills, it is a good plus to the research skills possessed by a product manager. With the information at hand, the best use of it can only come from deep, and thorough analysis, therefore well-honed analytical skills are also crucial for the same.
  6. Marketing Skills: If you built something it’s time to sell it, and you cannot do it without the appropriate skillset. Be it your team’s expertise, your company’s spotless record, or pitching a new project to the stakeholders, it all falls on the product manager’s shoulders. Therefore, shouldering such great responsibility, it becomes even more important to possess fine marketing skills to keep it covered.

Though there is no end to learning, but with the above skill sets covered, you will pull off a great deal as a product manager, and if you feel something is still lacking, there is always a scope of improvement and continuous learning.

FAQs

  1. What is one technical skill you must have as a product manager?

Ans- Jumping into the technical, its good to have the skills of research and analysis under your belt to be an efficient product manager.

  1. What does a product manager do in analytics?

Ans- Analytics pulls the data and draws out meaningful facts out of the same, presenting the information in easy to use, and understand format.

  1. What qualifies a good product manager?

Ans- A good product manager needs to be skilled, manage the team efficiently, be a team player, and also be the leader that takes the team forward.

  1. What is the strongest skill set as a product manager?

Ans- As a product manager, understanding the business environment, the trends and technology can be a great plus to work around the job.

  1. What are the top 3 responsibilities of a product manager?

Ans- A product manager sets the goals, vision and roles of the team. He also acts as the bridege between the client and the higher-ups, and also the team he is leading.

Categories
Data Analytics

The Future of Data Analysis: Emerging Trends and Skills You Need to Know

When it comes to data analysis, you cannot expect to be stuck in one spot, change, evolution, and growth are all but a part of the analysis. The techniques and tools you use today might be ineffective tomorrow. Therefore, it is important to be dynamic, and flexible and go with the trend. You need to tweak your approach for the best results and to stay one step ahead of the competition. To do this, you need to be well aware of the transforming trends. Catering to the demands of the same, insights about some of the emerging trends and skills has been given below:

1. AI based Data Analytics

Coming in hot, the trend of AI can take the entire industry by storm. The business world is revolutionising and AI is one of the factors behind it. AI has improved the entire process of data visualisation and data analysis, faster than manually done tasks.
AI has taken effective algorithms, new patterns, and other advanced technical solutions to give dynamic and flexible outlooks to data analysis. Beating the run-down traditional approach which was both monotonous, time-consuming, and inefficient. Therefore, it is time to make the switch and hop on to the trend of AI.

2. Cloud Technology integration

Cloud technology is the answer to most of the modern world’s problems, and it is fat enough to answer the call of the data analytics industry as well. While public clouds are unsafe, and private cloud servers are too expensive to own, a hybrid cloud server comes to the rescue. Offering the feature integration of a public and a private server, it offers a centralized database, data security, scalability of data, and much more at a cheaper cost.

3. Edge Computing

One of the most interesting trends to look forward to is definitely in the domain of edge computing. Unlike the traditional approach of sending the data to a central location before it gets processed, this method takes away the need to do the same, analyzing the data right where it is created, thus enhancing efficiency.
Offering better efficiency, and working on the security of data as it doesn’t need to be transferred to different sources. Therefore, for businesses looking for effective and efficient solutions, edge computing can be the answer.

4. XOps

With the widespread use of AI and data analytics in all types of organisations, XOps has emerged as a key component of business transformation procedures. DevOps, which combines development and operations, is where XOps got its start. DevOps best practices are used to enhance business operations, efficiencies, and customer experiences. It seeks to guarantee repeatability, reusability, and dependability as well as a decrease in the duplication of processes and technologies. In general, XOps’ main goal is to provide flexible design and agile orchestration in conjunction with other software disciplines to enable economies of scale and assist organisations in driving business benefits.

5. Data Visualisation

It is all thanks to changing consumer preferences and corporate intelligence, data visualisation has quickly gained traction. The final mile of the analytics process is sometimes referred to as data visualisation, which helps businesses understand large volumes of intricate data. Businesses can now make decisions more easily by utilising graphically interactive methods thanks to data visualisation. By enabling data to be seen and displayed in the form of patterns, charts, graphs, and other visual aids, it impacts analysts’ methodology. Given that the human brain processes and retains images more readily than text, visual aids are an excellent means of forecasting future business trends.

Conclusion

In the end, these trends and tools are all foresight, a way to leverage the skills and knowledge to understand the work better, get more efficient, and increase the output. By hopping on to these trends early on, organisations can pull ahead of the competition and make a mark for themselves. Not only does it offer them a competitive advantage, but increased efficiency helps to get more work done in a much shorter span.

Categories
Data Analytics

From Insight to Action: Building Your Data Storytelling Skills for Business Impact

The basic fact that data analytics generates rich information, is the reason why it is becoming more and more important in the market. The end goal is to take appropriate action based on the insights gathered. Business professionals who determine they must communicate difficult findings to completely comprehend the entire tale behind and ahead of the analysis are the primary necessity for this. This is when the real market gap appears.

The data is not just about the expression, or insights drawn from data analysis, but it also requires a clear and transparent representation of the same, hence the need for visualisation. To cover this aspect is where data storytelling pops in to cover this particular aspect of data analysis.

Crafting a compelling data narrative

Crafting a data story makes use of certain elements, all of which need to be strongly in place for an effective and impactful visualisation effect to take place. Some of these elements have been listed below:

  1. Characters– It is important to understand the customers who will be impacted by the setting, and the people who will be interpreting that scenario. Working along the lines of, identifying the key players in advance can be a great tactic.
  2. Setting– For a good narrative, you need the right environment, and the right stage to be set for the story to unfold. Therefore a lot goes into the background building of the story you will be working on. Visualisation in this regard can also show how the story progressed in that area over the speck of time.
  3. Conflict– In the middle of all the representation, it is crucial to focus on the target problem itself, to not lose sight of it. Therefore shedding some light on the same, and making it a part of your representation is also an important aspect.
  4. Resolution– What use is the listing of problems, if it doesn’t come attached with a solution? Working on the same, it is better to attach a solution to the problem statement as a concluding part of the storytelling.

Benefits of Data Storytelling

It’s better to learn about the benefits attached to something before you start working on something, it helps in better understanding of the expectations from the same. Some benefits of data storytelling have been listed below:

  1. Efficient Decision-making– With a better understanding of the business already covered by you, the next step as to what to follow would come effortlessly to you. With all the data present right in front of you, ready to be worked upon, you can keep going, with much more confidence and approach the same.
  2. Powerful Analysis– Everybody runs the risk of losing potential clients to competitors in a highly competitive market. Finding competitive data insights from your marketing and operations will help you stand out. You can utilise your internal data to create a narrative about the character of your business and its distinctive approaches, which you can then market as a way to help your target audience with their problems.
  3. Improved Client Engagement– To foster better connections and relationships with the clients, it is imperative to understand the client’s requirement, and position yourself accordingly. For building effective client communication with clients in every manner like compiling information into a presentation, data storytelling can save you when it comes to effectively conveying the results and demonstrating the value of your services.
  4. Enhanced Visual Appeal– Given that people tend to have shorter attention spans than they used to, data visualisations can be very helpful tools for conveying important information. The goal of using heat maps, graphs, charts, and other visual aids is to make the final product visually appealing to the user. Instead of merely seeing the raw data, clients want to hear the success story that led to the outcomes. When reporting to clients, combining facts, expertise, information, and insights in a visual manner can be quite beneficial.
Categories
Data Analytics

Beyond Spreadsheets: Mastering the Tools Data Analysts Need to Succeed

If you thought spreadsheets were all that you needed to be an expert data analyst, you are in for a heart-breaking surprise. While spreadsheets might be a part of data analysts’ arsenal, it is not the only thing keeping their boat afloat. To be a successful data analyst, diversity in terms of the skillset needs to be there. These tools bring flexibility, expand your domain of effectiveness, and reduce reliance and wastage of manual hours

1. Spark

It’s not just you that needs upscaling, but you do too. When handling major chunks of data, Python and SQL will hit their limits. In times like these, it is important to bring in the big guns to handle bigger datasets. One such tool is Spark, allowing you to handle clusters of data by splitting it up across nodes, and working on the same. It is easier to split the data and work on it in chunks, rather than handling all of it at once.

2. Python

Python is one of the most versatile and easy-to-learn programming languages out there. If you are entering this field, make sure you are well-versed with Python. The use case and wide range of applications that come attached to it make it even more relevant. There are endless possibilities to work with Python, be it any field, or any work area you choose.

3. SQL

SQL is an easy to work with database of information, making not just our work easier, but simplifying a truckload of the same. SQL is that best friend that you cannot make if you are looking to enter the field of data analysis. With an easy-to-learn syntax and lots of upside when it comes to working, there is a lot of use that can be drawn out of SQL, especially in terms of data segregation, extraction, and sorting.

4. Julia

It’s not just a fancy name given to a tool, the young-generation language might be pretty new-age based, but it has captured a fair share of the market on its own, due to the competency it offers. One of the best picks for scientific computing, machine learning, and data mining tasks, Julia is already taking the analysis industry by storm. In some areas, the popularity of the same has risen so much, that it is giving Python a run for its money.

5. R Programming

Next up on the list is R language. Though R might not be the first pick for a lot of people, only well-established analysts know the impact it can create well effectively clubbed up with Python. Be it tasks revolving around data science or advanced computing, with the help of R programming, it can be done in the blink of an eye.

6. Excel

One of the most basic skills, the use of Excel is spread across all sectors, and all sorts of work-cases can be sorted out with this one single tool. Excel has swiftly made its place known among professionals because of its easy-to-use interface, greater applicability, and effectiveness. Even with the rise in those fancy tools for analysis, no replacement for Excel has been found so far.

7. Power BI

Bored of working and dragging yourself across those default cells of Excel, and need a little bit of spice in your data analysis workspace, it is time to make the switch to Power BI. Bring in data from multiple sources, put them in place, analyse them, draw valuable insights, and present them through simple, yet clear visualisations, all with the help of Power BI.

8. Tableau

The list doesn’t end here, at least not without the mention of Tableau. As if the tools listed above weren’t enough, Tableau can surely be the one-stop solution for you. Offering a similar application and capacity to Power BI, it pulls through when it comes to beautiful, impactful, and powerful visualisations. Tableau comes packed with a lot of unique and powerful features unique to this tool alone.