Tips to Negotiating Your Salary

salaryMany are times we are presented with opportunities to negotiate our salaries be it over email, over the phone or in person. Whether you are working for, an office job or working as a freelance, salary negotiation is always a cycle that occurs throughout your career. At any given time in your career, you will be interviewed for a new position, negotiate a job offer, leave your current position, start your new job and eventually ask for a pay rise.


How to negotiate your salary

Salary negotiations start right from the interview process. It begins at the moment you are asked about your current salary and the expected salary. The first rule of salary negotiation is not to disclose your salary history or salary requirements as it can turn out to be uncomfortable. If you are negotiating your salary over email, you are likely to get the first offer.

Once you get the first offer, you will counter offer by providing a witfully crafted email that includes a strong case supporting your counteroffer. In most cases, your counteroffer will be 10 to 20 percent more than their offer. After you send your counter offer, you are required to think past the response you will get from your potential employers. The recruiter may come back with a new improved offer, and this presents you an opportunity to refine the final details of the proposal, accept it or decline it.


Negotiating Tips

negotiateAs long as you have done the necessary research to figure out the realistic salary range, the next thing you need is a plan for navigating the negotiation. All the other stuff will always fall into place. First, you need to have a salary range instead of having a single figure. Offer this range based on what others in the field are getting. When you have an acceptable salary range, you set yourself up to negotiating and finding compromise easily.

Note that you should not sell yourself short. One key issue people make is failing to include benefits in their total compensation. An example can be when you earn $120,000 per year with a 10 percent bonus on dental, health and other additional benefits, you should respond to the question by saying you look forward to getting $132,000 plus generous benefits.

It is also essential to practice your pitch at least once before the actual negotiations. Find someone who can listen to your proposal as you pitch, so that they can help you refine minor details in your pitch. Always be gracious and don’t be worried too much about coming out as demanding or ungrateful. When it comes to negotiation, it is also essential that you be confident in your delivery, and no matter the outcome, be appreciative and understanding of the opportunity.

There are also important issues that you should have in your mind. When you get an offer, ask if there is a chance to negotiate it. Ask questions for clarity such as whether there are any other benefits besides the base pay. Ask about the outlook of the salary raises and promotions. Also be sure to ask the metrics your employer uses to evaluate the success of employees.

How to Start a Data Science Company

data scienceLearning data science can be complicated and intimidating. Let alone learning data science, starting a data science company can be very overwhelming. Before you can launch a data science startup, you need to be a data scientist; else you will be joining in as an investor. There are lots of questions you have to answer to get everything right.

Which tools and languages do you need to learn? Is it R or Python? What kind of techniques do you need to focus on and how much statistics must you learn? These are just some of the critical questions you have to get right from the onset. We have created this guide to help those who want to come up with a data science or data analytics startup.

Choosing Roles

In the data science world, there are many different roles, and the first thing you need is to choose the right role. These roles include a machine learning expert, a data visualization expert, a data engineer, data scientist among others. Your background and experience will help you fall into the right category. It would be difficult and costly to shift from one category to the next.  If you are not sure about what to choose, talk to people in the industry who understand these roles well.


Services to Offer

If you want to start a data science company, you must provide analysis services to help solve your customer needs. You can sell your services to one customer, and if you deliver quality services, the next customer will come calling. You do not need to worry about getting other customers, and the kind of work you provide with the first customer determines how your success will roll out.


Get the Right Team

teamworkRunning and delivering on the services required in a typical data science company is never an easy work. The market for data science is full and ripe but suffers skilled personnel. There are not enough qualified people to take up data science roles. You need to take time to assemble your ideal team that can deliver in an overwhelming environment. Take your team to task, to keep them motivated and always ready to learn new and emerging technologies.


Customer Experience

The customer is king, and their satisfaction will play key between your success and failure. When starting out, you don’t need to worry about growing your customers from the offset. What you need is to pitch to one client, bring them on-board and work to solve the problems they are facing. And since many similar clients are facing same issues, words will spread like wildfire that you can deliver on your promises. Referrals are one of the best and effortless ways to grow clients.



You can hardly survive in the data science world without funding. You will need to invest a substantial amount of money in building your data infrastructure and centers. You will also spend heavily on product development and testing. Marketing budget equally deserves its share. You will also need to pay your employees, be it in monthly retainers or to have some equity in your company. To run all these issues comfortably, you need to look for investors ready to finance your business.







How to Become a Data Scientist

data scientistData science has been branded as the hottest and sexiest job of the 21st century. The modern world we are living in today is full of pressing questions that must be answered by big data. The term data science comprises data analytics, business intelligence and much more. For businesses and nonprofit organizations, there is a seamless infinite amount of information that can be collected, sorted, analyzed and interpreted to help in making meaningful decisions.

A good question is how a business use purchasing data to create a marketing plan can? How can various departments in government use patterns and behavior to engage in community activities? For these and many other questions, we can only rely on big data analytics for answers.


Who is a Data Scientist?


A data scientist is a person trained to gather, organize and analyses data to help people from different industries make appropriate decisions, Data scientists come from different educational backgrounds be it math, computer science or engineering. A data science degree involves a range of computer-related majors but also features math and statistics. To become a data scientist, there are some natural skills you need to possess. First, you need to be curious and have a drive that pushes you to learn always.


Getting Started

To get started, you need to know the role of data scientist and what they play in the industry. Next, you will get yourself acquainted with python. When you get to know the basics of python, the next step is to explore statistics. When examining statistics, you should aim at having a firm grasp on the basics of statistics. You should also be ready at exploring a given dataset and performing their respective data visualization.

Next, you need to get into the basics of machine learning. In the end, you should develop enough knowledge to take part in hackathons and get a good ranking. Go into the depth of feature engineering as it forms one of the most exciting aspects of data science.

Data Science Persona

At this stage, you need to build a data science persona. The real challenge facing data scientists lies in explaining the power and capabilities of the models you create for non-technical people. Build your persona and work hard to get recognition and ranking in the groups you compete with.

Once you have built your persona, go into the depths of advanced machine learning and time series modeling. You should aim at tackling advanced ML algorithms and time series models. Note that most of the data you will deal with will be unstructured.

Once you get to understand how to deal with unstructured data, it’s time you introduce yourself to deep learning. At this stage, you get to know how to deal with neural networks and how to solve neural problems. Remember practice is the only real way to keep up with the demands of big data. A data science degree might be the most obvious career path, but other non-technical computer-based degrees can help you in launching your data science career. These degrees are in the fields of computer science, statistics, physics, social sciences, math, and economics.