Analytics - Finance, Tech & Analytics Career Resources

Top 9 Soft Skills Every Employee Must Possess in the Age of AI

Posted on October 1, 2020 by Imarticus Learning

It is the year 2020. The technological world is brimming with new concepts and ideas. One such concept that has been undergoing a series of upgrades is, Artificial Intelligence.

For decades we have been trying to imbibe human-like emotions and features in machines. Off late we have seen some successful AIs in the market. Artificial Intelligence has been making waves in the world of technology and is making its presence felt in places like social media monitoring, proactive healthcare management, automated financial investing, smart assistants, virtual travel booking agent, robot manufacturing, disease mapping, and the list goes on. Be it your social media feed or your Google search, there is AI in the background. Artificial Intelligence courses are also being offered by the top universities since more and more people are taking a keen interest in learning AI.

While we are trying every possible way to make machines behave more human-like, there will be a time when machines will take over the industry replacing humans. Since machines will be doing most of the technical and automated work, humans must look forward to inculcating more humane qualities in order to be favorable employees in the future. With hard-skills being induced in robots , humans have to acquire soft skills in order to be able to communicate and build better relationships with people at workplaces or otherwise.

Here are the top 9 Soft Skills that Employees will be requiring in the age of Artificial Intelligence:

Creativity: Creativity is intelligence having fun, Albert Einstein said. It is an important aspect of intelligence since it helps the employee come up with fresh ideas. Creativity is like a breath of fresh air. It doesn’t necessarily have to be poetic or artistic. Any novel idea can be attributed to creativity. No matter how advanced a robot can be , it definitely cannot pick up a skill like creativity.
Analytical Thinking: In easy words, analytical thinking is the ability to observe a problem and provide a solution with logic and reasoning. Analytical thinking is essential in making decisions and problem solving. Observation is one of the most key aspects of Analytical thinking. Developing this skill will always keep you a step ahead and make you a potential employee at the workplace.
Emotional Intelligence: As important is Intellectual ability, emotional intelligence (EQ) is crucial for a happy and successful life. Emotional intelligence helps you build stronger connections with people around you. It helps you in maintaining bonds at work and can help you accomplish your goals better. EQ also comes in handy while making sound decisions since it helps you distinguish between things that matter to you and things that don’t. Given a robot’s inability to build relationships with humans, EQ is an important skill that will help employees get better feedback at work.
Interpersonal Communication Skills: Some people have innate communication skills and can start fruitful conversations with their peers. The ability to have an engaging yet informative communication skill is mostly inherent but that doesn’t mean that it isn’t possible to polish this skill in case you’re lacking in it. How you say something is as important as what you say. Demeanor, facial expressions and body language are elements that need to be worked upon.
Active Learning with a Growth Mindset: The hunger to learn more and get better everyday is a skill that has to be inculcated in ourselves. One way to have a growth mindset is to keep learning. Look at challenges as opportunities and strive to overcome them. The day you decide that you know everything, that day you’ll be of no use to your employer. Hence learn and expand your knowledge. Keep improving your skills and you’ll always be a valuable employee.
Judgement and Decision Making Skills: It’s no doubt that machines are able to process far more information than humans but they lack the skill of making sound decisions that humans are capable of. An employee must instill decision making ability to reach effective conclusions.
Leadership Skills: A leader must possess qualities like honesty, confidence and the ability to make decisions. A leader must be able to come up with solutions to the common problems encountered at workplaces and motivate employees to perform better.
Diversity and Cultural Intelligence: With the industry going glocal and people of diverse backgrounds coming together to form a team, it is important to embrace diversity and create an inclusive environment to work in. Cultural Intelligence (CI) is the proficiency to work in culturally diverse spaces. Every individual has his own unique way of approach towards the world, working in a diverse environment will ensure more effectiveness in decision making.
Embracing Change: Change is natural and it’s important to approach change with an open mind. Accepting changes help you to become flexible and helps you to adapt better. In the age of AI, change is inevitable hence you should always look at it with a positive attitude, shape yourself better and align with changes around you.

Also Read:

Top Artificial Intelligence Trends For 2021

Posted on September 30, 2020September 6, 2021 by Imarticus Learning

The coronavirus pandemic has impacted the global economy like never before. However, people are doing their best to adjust to the current scenario and make the most of their possibilities. For instance, it makes us realize the significance of artificial intelligence, now more than ever. The usage of self-teaching algorithms, coupled with smart machines, will make our fight against COVID-19 easier.

Artificial Intelligence Trends 2021

Even though the focus on AI was slightly diverted because of the delays in development across the lockdown period, there is nothing that can stop the possibilities in the future. Let’s take a look the artificial intelligence trends that will dominate 2021:

Efficient Data Analytics and Insights
Have you wondered how the governments across the world kept a record of the number of growing transmissions? Global health institutions, educational research centers, and governments have collectively joined hands and used advanced technologies to keep regular track.
Technology is the reason why the pandemic hasn’t been as tragic as it could have been. For instance, the 1918 Spanish Flu killed almost 50 million people globally. Currently, we don’t just depend on scientific development but also on growing communication technology. In 2021, AI will be on the list of such developments and manage epidemics and pandemics more effectively.
Over 28,000 papers related to COVID-19, on medical and scientific literature, was published by April 2020. A search engine by natural language processing (NLP) algorithms is available online. Thus, anyone searching for AI assistance will get this dataset readily available.
Solution through AI will also help other severe medical conditions like cancer; there has been a harmful impact on other medical concerns due to the primary importance of coronavirus. AI will dedicate to healthcare in a broader aspect than just tackling a pandemic.
Automated Detection for Prevention
During the lockdown, drones helped the government keep a check on people not maintaining social distancing. Soon, drones will have the ability to detect coronavirus positive patients by reporting people with a high temperature in a crowd. Drones will use computer vision technologies to analyze data caught on cameras. The reports will help local authorities quickly find the possibilities of virus spread.
Again, using facial recognition technology will become more familiar with the help of computer vision algorithms. Although the concept is controversial due to privacy concerns, the system helped police catch people escaping quarantine. It will also track movements made by people who have symptoms when they are amidst a crowd.
Business to Go Through Digital Transformation
2020 changed the way we work and socialize. While many industries are still in a shutdown mode, digital business growth is at an all-time high. For example, Amazon made record sales going 40% up from last year’s sales.
With the help of AI tools, businesses can grasp changes in customer behavior. Companies that didn’t understand the impact of digital presence are now switching over to understanding behavioral analytics.
Tools that give access to self-service will prevail much more in 2021 than what it is not. Thus, AI will be a part of small and medium-sized organizations looking to push their competitiveness.
Artificial intelligence Can Stop a pandemic Before it Starts
Do you know that AI provided us the earliest reports of the coronavirus pandemic? Yes! It is true. A tool by BlueDot, a Toronto-based company, found out about a possible outbreak while scanning 100,000 reports from media sources.
AI algorithm focuses on the future. Before another pandemic, technology will not only predict possibilities but create ways to combat its spread.

Breakthroughs in the artificial intelligence course will alert us about such dangerous outbreaks and help curtail it. However, along with technological development, global politics will also impact the course of advancement.

Also Read: 10 Interesting Facts About Artificial Intelligence

What is the Role of Data Scientist?

Posted on September 29, 2020September 30, 2020 by Imarticus Learning

A data scientist is someone who systematically organizes the large volume of raw data in any given field. This individual will then analyze the entire specifics to process it according to the needs and present it in such a way that helps solve a problem. The overall objective here is to improve the business prospects for the stakeholders. Though each field differs in their requirements the basic role of the data scientist remains the same.

What does the role demand?

This particular role demands skill in various fields such as computer science, mathematical, analytical, and statistical skills. Today, there are unlimited raw data available from various sources. A skilled scientist should be able to analyze and compile them into a useful form for the field they work in.
In order to identify and use the information in the right manner, all these skills would be necessary for the various stages of this analysis. Here are the major stages of data analysis by scientists.

Research: The primary stage is doing the research on the specified details and identifying which is relevant and what to ignore. This step could be termed data mining which literally means to dig deeper to find the best and relevant input.
Once the relevant details are identified, the next step is to acquire it. The information is scattered and it needs to be collected, cleaned, and duly and specifically stored according to the requirements. This will help with the next stage of data analysis.
Analysis: The analytical stage involves several processes where the scientist will have to use the information and implement the algorithms and other skills. From the collected details, the scientist must identify the scope and methodology to transform it into the specific model for usage by the business. The ultimate aim is to make use of the data in a beneficial way. So, this analysis and integration of input into a workable model is an important step.
Design: The final stage is where the collected facts and figures undergo processing to make it suitable for the final implementation for the business. It is processed and designed in such a way to present the best model. Prior to its presentation, the analytics specialist must evaluate the results and review them properly. Once presented the scientist would then collect feedback from the stakeholders to change it to the best version for the field. Though this stage is not exactly about any design, the data scientist is responsible for the model making and its final output. This role demands the application of the various techniques learned by the individual and the right usage of the same at the key areas.

How to qualify for this role?

Anyone who has a basic knowledge of computers can learn the field and be a data analytics specialist. Even if there is no formal education in computer science, there are various degrees and certifications courses available to qualify for the role.
One can enroll for any data scientist course to polish the skills and qualify for the role.

Conclusion

The role of a data scientist is performing the processes in repetitive mode but the overall work specifics changes according to the requirements and feedback. The role also calls for good communication skills and a creative mind to present the data in the form of compelling storytelling. This is a great career opportunity at this juncture since everything revolves around the abundant data available and utilizing them into a useful model is a real skill. If you are planning to fit into this role, start developing, polishing, and practicing your skills now!

We offer data science course at our centers in Mumbai, Thane, Pune, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.

SQL For Data Science: One-Stop Solution For Beginners!

Posted on September 29, 2020March 26, 2024 by Imarticus Learning

Data science has earned the reputation of being the most promising job of the times, even during this pandemic crisis. With the current changes in the global business and economic background, data science has proven to be a more relevant career opportunity. If you are following the subject and have a keen interest in making a data science career choice, you must have heard about SQL as well.

SQL is used to access and manipulate data. It helps to store data, access whenever you need it, and retrieve if need be. SQL training will give you a much-required head start in the highly competitive job market.

Why is SQL Important in Data Science?

Today’s business decisions are data-driven. Data is generated all through the day, across the globe. The amount of data generated every day is simply astonishing – about 2.5 quintillion bytes. This underlines the enormity of the subject we are dealing with.

Now that data is available, what is the next thing? How are you going to make sense of this huge amount of data and use it to make a decision? Data science steps in here. You need to collect, organize, and process them to make sense of the data and to derive insights. To do this, you need tools. This is what SQL does. It is a querying language used to store, access, and retrieve data.

What is Structured Query Language (SQL)

SQL is a language that is primarily concerned with managing relational databases. SQL is the typical API for such data tables. While using SQL, data can be accessed and managed without changing the databases. You can perform a variety of actions including updating, querying, deleting, and inserting data records. Oracle, MySQL etc. are examples of such databases which use SQL.

SQL works based on some simple commands that are associated with different data tasks. These commands can be used to create database and tables, insert, delete, or update data, to alter table and database, drop table and index.

How to Create a Table Using SQL

Let’s see how to create a table using SQL commands. Remember to use UPPERCASE letters for SQL commands, and use semicolons to terminate commands.

You may follow the steps given below to create a database.

Step #1 Creating a Database using SQL

CREATE DATABASE: Use this command to create a database “Test”.

USE: This command activates the database.

CREATE test;

USE test;

Your database named test is ready and activated.

Step #2: Creating a Data Table

It is as easy as typing a command to create a table, just like the way you created the database. All you need to do is to decide on the variables you want to include in the table.

Suppose you want to create a table with the following features:

Serial Number (SL)
Purchase item
Cost
Number of pieces

You can use the command CREATE TABLE to create the table. The four features of the table are SL, purchase item, cost, and number of pieces.

Now, to create the table, use the command as given below:

CREATE TABLE cart (SL NOT NULL PRIMARY KEY AUTO_INCREMENT Purchase_item TEXT, Cost INTEGER, Number_of_pieces INTEGER);

You might have noticed that we have given the value we are going to provide for each feature. The Serial Number is a primary key, which means it represents a unique data. The purchase item will be entered as text, while cost and number of pieces will be entered as numbers.

The table is now ready with the field names and the value to be entered to each cell of the table. To see how the table is executed, type the command “DESCRIBE cart”. This will give you a display of a table with the given features.

Field	Type	Null	Key	Default	Extra
SL	Int(11)	NO	PRI	NULL	Auto_increment
Purchase Item	Text	YES		NULL
Cost	Int(11)	YES		NULL
Number of pieces	Int(11)	YES		NULL

Step #3: Data Input

Once you create the table, you need to enter data into the respective fields. To do this. Use the SQL command “INSERT INTO”.

To insert values, follow this pattern:

INSERT INTO cart VALUES (NULL, “Rice”, 75, 10)

The “null” value is assigned to SL, as it will follow the command and auto_increment from 1.

The entered value will look like:

SL	Purchase item	Cost	Number of pieces
1	Rice	75	10

Follow the same pattern to enter more values.

Data Science is trending these days. Getting trained in a skill that is much in demand improves your chances of getting hired manifold.

So, choose a good data science course and give your profile an extra edge while competing for career opportunities.

What is the Career Path for a Data Analyst?

Posted on September 24, 2020 by Imarticus Learning

The world has moved from an operations-oriented setting to a data-oriented setup. This movement has given rise to increasing demand for people who know how to manage data and draw useful insights from the same. With major disruptions in the technological space, more and more companies are demanding people who know how to manage data. A data analyst is one such person. He is known for his expertise in the field of data management. He is also responsible for converting data insights into simple business activity statements for easy implementation.

Industries where a data analyst can work

A data analyst can work in different kinds of industries. As technology is disrupting, all kinds of industries are looking forward to making use of this disruption and capitalise on the opportunity. Some of the major industries where a data analyst can work are as follows:

Banking: This space has witnessed some of the major transformations in the way things work. With the help of data, everybody can now use mobile banking with satisfaction
Media and Communication: A data analyst can provide different insights on campaign performance, consumer preferences, etc.
Healthcare: This segment is the new elephant. Healthcare with the integration of data is expanding like never before.
Education: EdTech companies use different artificial intelligence techniques to come up with innovative solutions in the education space.

A data analyst can also apply to several manufacturing firms as the automation process are kicking in. A data analytics course will help you in understanding what kind of jobs do data analysts perform in several companies.

The career path of a data analyst

A data analyst has a lot of potentials. He starts from an entry-level role and learns a lot from his experience. He then makes use of these experiences to climb up the ladder. So, the path traversed by a data analyst is as follows:

Data Analyst: A data analyst is an entry-level position for a person who has sufficient knowledge in the field of data science. He is supposed to know preliminary programming languages like R, Python, etc. Also, the average pay of a data analyst is around 6 Lakhs per annum
Associate Data Analyst: As soon as a person masters his job as a data analyst, he is promoted and made an associate data analyst. An associate data analyst earns about Rs.11 Lakhs per annum.
Senior Data Analyst: Reaching this level is difficult. Also, the more steps you take on the ladder, the more difficult it gets. A senior data analyst must be a master of programming languages. A senior data analyst earns around Rs.13 Lakhs per annum.
Product Manager: The company promotes a successful senior data analyst to the post of a product manager. Becoming a product manager is quite hectic. However, the role comes with great learning as well as a great learning opportunity. A product manager in India earns about Rs.16 LPA.
Lead Data Analyst: A lead data analyst comes on one of the top levels of the hierarchy. People expect him to have a cloud computing experience. In India, the average pay is around Rs.20 Lakhs per annum. Lead data analyst is then promoted to a very senior position and can be called a director or vice president based on the norms laid down by the company. At this post, a person earns around 40 lakhs per annum. However, the figure is different across companies.

You can reach at the top in less time by brushing up your skills. An online course would help your cause. Becoming a data analyst is an achievement in itself.

Also Read: What are the Salary Trends in Data Analytics

What Does It Take to Be a Good Data Scientist?

Posted on September 24, 2020 by Imarticus Learning

The world is evolving at a fast pace. Technology is one of the major contributing factors which is transforming the world. You can spot a data scientist on the driver’s seat of this change vehicle. A data scientist is not only good with handling data but also knows how to derive insights from the data. You can take up a data science course to understand the nuances of the job.

Skillset Required to Become a Data Scientist

A data scientist must possess a diverse skill set to handle complicated tasks and activities. His insights will contribute to the growth of the organisation. Therefore, a data scientist needs to understand the different aspects of the business. Listed below are some of the skills that a data scientist must have:

Must have a good knowledge base of Mathematics or Statistics
Must be aware of the different machine algorithms and should also know how to write one
Should know how to write codes on programming languages like Python, R etc. because the role involves frequent usage of different software
Should be equipped enough to manage large data sets and extract conclusions and outcomes from the same
Should have the required domain knowledge
Must have an analytical bent of mind and problem-solving ability

To get a better insight into what skills should a data scientist have, you can take a data science course. The course will help you in understanding the kind of work a data scientist is required to do and how to become a better data scientist.

The kind of work

Companies ask data Scientists to perform a lot of functions. Some of these functions include:

Modeling of Data
Data Visualisation
Drawing business insights and implementing them
Business Interaction
Coding
Engineering of Database
The building of new systems and techniques for solving common issues

Becoming a data scientist is not an easy job. You will have to brush your skill sets or update them for getting ready for the market. Upskilling is important in case you want to become a successful data scientist. Companies are on a hunt to recruit people who are good at the job. Enrolling for an online course is the best way to upskill.

A lot of people also prefer engaging in self-study and train themselves in a particular area. With the increasing automation across industries, data scientists are in huge demand. The skills which are gaining momentum are listed below:

FinTech
Blockchain
Robotics
Cybersecurity

The list is not exhaustive. Data Scientists and the jobs related to it are here to say. Companies need data scientists to keep up with the technological changes and disruption in the innovation space. This brings in a need for building your network and growing by interacting with the people belonging to the network. This also helps in gaining insights about the industry.

Applying for a data scientist position

The network which you build while preparing to become a data scientist will help you a lot in getting a job. Therefore, networking plays an important role in the life of data scientists. Social media also helps in landing a job as a data scientist. An undergraduate can also become a data scientist but he will only be given an entry-level role. Postgraduates tend to get better roles in a company. Also, these professionals are paid way higher than the professional who just has an undergraduate degree. Everybody can enrol themselves in an online or an offline course to upgrade skill sets and become a data manager.

Edge Vs Cloud: Which Is Better For Data Analytics?

Posted on September 21, 2020March 21, 2024 by Imarticus Learning

What is Edge Computing?

Edge computing is a segregated topology which serves to bring processed information closer to the device that is gathering the data rather than relying on a central unit which would be located much farther away.

What is Cloud Computing?

Cloud computing involves the process of delivering important information and services such as storage without the need for involvement of active management.

Which Out of the Two Is Better For Data Analysis?

In today’s world where AI has become an extremely important part of our lives, developers are looking to merge the devices we use on a day-to-day basis with artificial intelligence to make running businesses easier for organizations.

In such cases, we must look at the various computing methods that can make this possible in an efficient manner. Here, you would think that cloud computing would hold an important position in making the most suitable and ideal decisions. Platforms which are based on cloud allow developers to quickly create, deploy and handle their applications.

These would include playing the role of a platform of data for applications, application development which would help bridge the gap between data and users, and so on. It is popular for its flexibility with data storage and the ability to perform analysis processes.

On the other hand, edge computing allows applications and various other analytical and service processes of data to be done away from a central data unit, bringing it nearer to end-users. It allows the processing to take place within the locally available resources, thus bringing it a step back from the intricately planned cloud model where data processing happens in specific data centres.

Let us dive into this further in detail.

Cloud vs Edge Computing: Latency Problems

Cloud computing is used extensively across various organizations and companies for data analysis. However, there may be situations where a business may face problems in collecting, transporting and analysing the data given.

When data is transferred to a remote cloud server, it allows the user to perform various complex algorithms with machine learning and thus predict the maintenance needs of a particular section. This is then forwarded to a dashboard on a personal system where one can determine what decisions are to be made further. This is all done comfortably from home or the office.

This is great, however, as one begins to increase the intensity of operations, one may begin to run into issues such as physical limitations on the bandwidth of the network and thus also latency issues.

Edge computing does a great job at reducing latency issues by involving a local server, maybe even on the device itself. The only difference here is that the issue with latency is solved at the expense of the processing power offered by cloud computing methods.

Businesses, with edge computing, are now being able to decrease data volumes which would need to be uploaded and stored in the cloud. This thus makes the process of data analysis less time-consuming.

Edge computing may still interact with other website applications and servers. It includes physical sensor thus allowing it to help run smarter algorithms and facilitate real-time processing which is used in smart vehicles, drones and smart appliances. It may not be as strong as a remote server, but it helps reduce the bandwidth strain that one would normally face with cloud computing.

A big data analytics courses would help equip a person aspiring to work in the field of data analysis with all the information that would be necessary. A big data analytics career is a good option because it is an ever-expanding field with a large number of opportunities!

What Does It Take To Be A Good Data Scientist?

Posted on September 18, 2020April 2, 2024 by Imarticus Learning

What does a data scientist do?

The importance and applications of data science have grown exponentially over the past decade. Data science is still in its nascent stage and there’s a whole lot to be identified about this discipline. Businesses have started implanting strategic decision-making tools that leverage data science.

Data helps businesses by providing them with hidden insights and helps them predict the future outcome of their decision. This helps organizations to make a better business decision.

Let’s delve deeper into what these data scientists do and how it helps the organizations.

Finding a solution to business problems

One of the most basic and key responsibilities of data scientists in an organization is to identify existing challenges and problems that a business is facing and finding solutions to remedy the situation. This might seem like a generic responsibility of every important professional but the main difference here is that data scientists use tons of relevant data to find the problem.

They try to come up with solutions after properly assessing the situation using various analytical tools that provide them with useful insights. They leverage statistical analysis, data visualization and mining techniques to provide effective solutions.

Find out relevant data using complex research

The 21^st Century businesses are complex than ever, there are various factors that determine the fate of an organization. With the number of complexities that exist, it’s very difficult to figure out what impacts your business and how it does that.

Data scientists simplify this for organizations by studying all variables affecting a business. They use complex research work to identify the variables that have a maximum impact over the business and which are highly relevant.

Identify patterns and trends

Another important work of a data scientist that helps businesses is to identify patterns and trends. Data scientists use sophisticated data analysis techniques to find trends and patterns from the data sets at hand. These data sets are generally historical records of the organization. It helps them to identify the existing patterns and trends which is used to make predictions regarding the future movement of the variables.

How to become a data scientist?

Data science is one of the most in-demand skills in the industry and given the wide range of applications that it has, the demand for a data science professional will continue to rise in the future. One of the most common questions in the minds of data science aspirants is how to become a data scientist? There is no specific answer to this particular question. It depends on what stage of your career you are at and the skillset that you have.

A data science course by reputed institutions such as Imarticus Learning guarantees placement with top-notch firms in the industry in addition to providing relevant knowledge and skills. It also helps you provide guidance from the industry experts who are highly experienced in this domain.

Let’s delve deeper into some of the most prominent skills for data scientists that you should hone if you are planning to opt for a career in this field.

Analytical skills

One of the key skills that are required in this profession and that forms the base of all your work is your analytical skills. One should have an analytical mindset and should be able to identify trends and patterns from a big chunk of data. You should be able to assess a situation from a different perspective to reach a successful conclusion. One should be trained to work with software like Python and R and should be equipped enough to handle large volumes of data.

Problem-solving skills

Another important skill that you need to work on is your problem-solving skills. You need to use data to figure out challenges that exist in the business. After you have figured out the problems you will have to provide a solution using data analytics tools that will help the business to achieve its goals and objectives.

What Do You See as the Key Strengths of a Business Analyst?

Posted on September 17, 2020December 5, 2023 by Imarticus Learning

A business analyst is in demand in all kinds of companies. Businesses rely on these professionals to provide insights that will be used in decision making and running business operations. A business analyst course will help a person in understanding the nuances of the subject and in becoming an effective business analyst.
A business analyst is chosen based on different parameters. Make sure to brush up your skills before applying for s similar role.

Key Strengths of a Business analyst

Business Analysts are known for their professional and soft skills. They need to be ready for the hard work and time the job demands. Some of the major strengths of a business analyst are as follows:

Identifying objectives: Business analysts are aware of the objectives of the business. Therefore, they work towards the goals set by the business itself. A business analyst knows how to do a job. Also, they know what kind of results have to be delivered.
Good communication skills: A business analyst interacts with different stakeholders of business regularly. Therefore, he must communicate effectively. A good business analyst knows how to talk to people and getting the work done. A business analyst course will also help in understanding the kind of tone required.
Strong reasoning ability: Business analysts are known for their acumen. Therefore, an ideal business analyst is someone who has strong reasoning ability and critical thinking skills. Also, these skills have to be brought in the day to day activities as business analysts manage huge data sets and points daily. Also, they have to extract a lot of information from these data sets. Good reasoning ability will help your cause. A business analyst course would help you with this.
Time Management: Managing time is one of the most important strengths of a good business analyst. The work schedule of an analyst is quite hectic. He has to manage multiple tasks at the same time. Therefore, an analyst needs to manage time as the job role demands commitment and high work volume. Also, it is important for a business analyst to multi-task. The job demands a full commitment to the schedule.
Good technical skills: Business analysts make use of different arithmetic models to come down to a conclusion. These models are developed for a better analysis of multiple data sets. Therefore, business analysts possess great technical and computer handling skills which makes their jobs much easier.
Analytical skills: The work of a business analyst requires him to acquire analytical skills as analytics form a very important part of the job. Also, with good analytical skills, you can reduce the time taken by every activity. Also, a major part of the business analyst job is to crunch numbers and data. Therefore, a business analyst needs to make use of the different analytical tools to come to a conclusion or to even analyse the data in the first place.
Decision-making skills: A business analyst is a respectable position in the company. They perform a pivotal role in managing the business. Also, a business analyst enjoys a great deal of autonomy. Therefore, he needs to make judicious use of his power and take decisions wisely. Also, these analysts are required to use their judgement in a lot of cases. Therefore, a good business analyst is most likely to have an impactful decision-making skill.

Conclusion

Business analysts are being demanded in all kinds of companies. They take up a professional approach to solve complex business problems. They play a very crucial role in running the business effectively and profitably.

Also Read: What is Life Like For a Business Analyst in India

What are the five must have qualities of a Scrum Master?

Posted on September 17, 2020December 5, 2023 by Imarticus Learning

A Scrum Master facilitates the inclusion of the Scrum methodologies to ensure smoother execution of the project plan. The roles and responsibilities of a Scrum Master lies somewhere between management and team leader. Finding a Scrum Master to fulfill the needs of the company is a very complex task, as the qualities needed for the role of a Scrum Master are carried by very few employees. The qualities vary from product to product, considering the unique features and the existing technicalities. Despite the individual requirements of the companies, there are five must have qualities of a Scrum Master:

Effective communication skills: The Scrum Master is a person who takes care of all the communication between the product owner and the entire team members, communication within the team, without actually being included in the strategy flow of the product. Considering the Scrum Master does not enjoy the authority and power to scare the people to achieve the objectives, the only way left is best communication skills. Here values and ethics plays an effective role. Giving respect and getting respect go hand in hand via effective communication network.
Healthy inter-personal relationship with team members (Community building and Servant leadership): Considering the Scrum Master understands the strength and weaknesses of the Product team members and the requirement of the projects, it is the core responsibility of the Scrum Master to guide the team members to remain organized, to speak out the problems and arrange the training and other such sessions as and when required. A Scrum Master may act as a team leader but does not enjoy the powers and authorities of a manager in real sense. The ultimate way to influence the team members is to command the respect.
In depth understanding of product and the related market: One of the key roles of Scrum Master is to understand the problems faced by the team members. Knowing the work-related technicalities in detail, a Scrum Master can help the team members with proper solutions without taking much time. Prompt and effective solutions help team members to remain progressive on the decided roadmap to achieve the goal.
Seeking continuous improvement: The ultimate objective of the Scrum Master is to improve the day to day working habits of the team members to improve the efficiency. The habit of an introspective and retrospective analysis of each and every process and procedure help the Scrum master to streamline and optimize the work as and when required. Scrum Master actually encourages the employees to step back and reflect to understand where they actually stand.
Understanding the difference between Servant leadership and traditional leadership: The role of Scrum Master is not to decide what a team is supposed to do, but to help the team with the support as and when required to achieve the objective by eliminating any impediments if present.

A servant leader is different from a traditional leader in a way that servant leader always thinks about the team members. The traditional leader serves the organization, whereas the servant leader serves the people in the organization. The execution of the management’s decision lies in the hands of employees, and employees tend to respond to those who think about them prior to the organizational goals. A servant leader understands that the employees not only work for the organization, they work for themselves too. Taking care of individual requirements to the best of the capacity is the role of a Scrum Master. Hence, a Scrum Master must have the core qualities of a servant leader, rather than a Traditional leader.

Also Read: Roles & Responsibilities Of Scrum Master

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