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How to Become a Data Science Manager

Without being cleaned and processed, data may not be useful to the business.

Question: Why does this team need a data science manager?

How to Become a Data Science Manager

Why Employers Need Data Science Managers

Going by the trends, there is a remarkable rise in the number of data science managers in the industry. This increase points to the fact that apart from having talented technicians in the industry, management skills in data science are also vital for the success of the objectives being pursued. Aside from this general observation, data managers are on demand for several other reasons.

  1. Data science is multidisciplinary. Data science is a broad field which incorporates computer science, statistics, and maths disciplines. At the center of these disciplines, data science employs techniques, concepts, systems, and algorithms to process data in order to gain the insight needed to solve problems. However, even with these powerful systems and programs, it takes human minds to make sound data-driven decisions and a data science manager to coordinate this process. In essence, data science is the tool that businesses use to enhance decision-making.

  2. The team is expanding by the day. The data science team is expanding. Unlike before, it may include a data analyst, business analyst, a data engineer, a data architect, data journalists, machine learning engineer, and other experts in the technology field. The size of a data science team depends on the needs of the business. To coordinate the tasks of such a team/department, a data manager is needed who has some expertise in the different fields along with management skills to steer the performance of the team.

  3. Companies have unique challenges needing unique resolutions. Data science may be a common study course with different specialties. Beyond the theory, its application is unique to different industries and businesses. Different businesses have different challenges which cannot be solved by one common program or algorithm in order to make a decision. For this reason, a data science manager is the person that will help identify the unique application of data science theory in search of solutions specific to the business.

  4. It takes both expertise and performance for success to be realized. While a data science team may consist of diverse skills, success goes beyond the expertise of the team members. The data manager is tasked with the role of driving the performance of the team to its expected best. Performance is pegged on attitude, motivation, ethics, and other soft skills.

The Role of a Data Science Manager

The data science manager is at the top of the data science team. He is not only responsible for decision-making and implementation of data-oriented solutions, but he also needs to understand the business landscape of the organization, identify opportunities in challenges and find a way of solving them together with his team in order to take the business to the next level.

  1. He plans data projects, designs database systems, and structures that align with the business objectives.

  2. They are responsible for testing new data systems and structures and well as verifying the effectiveness of recommendations resulting from data analysis.

  3. He sources talent, recruits, and onboard new members into the data science team.

  4. He defines the job description and scope of each team member, ensuring that they own the vision and purpose of the business.

  5. They are responsible for guiding the data science team.

  6. They liaise with other departments like engineering and product development, who need to use the processed data to make decisions.

  7. They are responsible for the monitoring and assessment of projects, communication of findings to business stakeholders, and offering direction based on these findings. 

What It Takes to Be a Data Science Manager

A data science manager needs to possess both technical and business skills. Some of the technical skills include:

  1. Math. Machine learning algorithms are derived from mathematical formulas based on algebra and probability theory.

  2. Machine learning. Machine learning is a data analysis model. It is an important tool that businesses use to make the most of the database available for their tasks.

  3. Programming languages. While it may not be possible to have an understanding of all the programming languages, a data science manager should at the least have an idea of which language to map to a given objective or task.

  4. Big data. Data science is all about data. Businesses are often faced with the need to extract insight and information from large complex volumes of data in order to make decisions. Analyzing big data takes special programming technologies and not the usual businesses analysis programs.

  5. Data engineering. As seen in big data, data is complex. Data engineering is the process through which data is transformed from its complex nature into a form that can be analyzed and understood. This happens through data scraping, ingestion, and cleaning, among other processes.

  6. Data analysis. Simply put, there is no data science without data analysis. Data analysis involves extracting useful information from a database, which is relevant to a specific task. It is, in fact, what a business counts on to make marketing, financial, and administrative decisions.

As a data science manager, you need to possess the following business skills.

  1. Communication skills

  2. Report writing and presentation skills

  3. Problem-solving skills

  4. Team and time management skills 

The Pathway to Data Science Management

There is no specific area that an individual needs the expertise to become a data science manager. However, not all qualifications meet the requirements of this role.

To become a data science manager you need

  1. An undergraduate degree in IT, information systems, information technology, computer science, or a degree in any other relevant field.

  2. Relevant work experience in data analytics, data engineering, data administration, database developer, or roles in related fields along with supervisory experience or experience in managing a project.

  3. Certifications may not be mandatory requirements, but they are definitely very important and a plus to your CV. You could enroll for a Data Science Certification Training and earn yourself a certification in Data Science. This will be an indication that you are keen on enhancing your skills.

  4. Consider pursuing a higher degree in addition to the certifications and work experience. 

Related Articles

Bounding boxes are one the most popular image annotation technique used to train the AI-based machine learning models through computer vision. It can be easily drawn and helps to annotate the object of interest in the pictures and make it recognizable for computer vision.

Bounding boxes used for object detection into many fields including self-driving cars, drones, surveillance cameras and autonomous robots and all sorts of systems using the computer vision. It helps to count the number of obstacles of the same class in a crowd.

How Bounding Box Works in Image Annotation?

Bounding box is a kind of rectangle superimposed over an image in which all key features of a particular object is expected to reside. The main purpose of using this annotation technique is reduce the range of search for those object features conserving the resources used in computing but helps to solve the computer vision problems.

Bounding Boxes for Object Localization

Computers can utilize the image classification or image recognition to simply detect the probability of an object in an image. While other hand with bounding boxes machines can visualize the images with objects localization that helps to solve the computer vision problem.

Actually, object localization algorithm produce the coordinates of the location of an object with respect to the image and using the bounding boxes to localize an object in an image is to represent its location helps computer vision problems in AI-based machine learning.

How Bounding Boxes Used as Training Data?

Bounding boxes annotated images are feed into the machine learning algorithms to identify the objects in the images and store them into the machine neural networks. And when huge quantity of such annotated images are used to train an AI model through computer vision the model give the predictions learn from these annotated images.

While developing the AI-based model for self-driving cars, drones, surveillance cameras and autonomous robots, bounding boxes are used to understand the objects by computers through machine vision that further helps machines to detect such objects in the images.

However, there are many other image annotation techniques used to make objects recognizable for the computer vision but it depends on the machine learning model training and AI project that requires certain types of training data for computer vision.

And as much as quality training data will be used to develop such models the predictions will be precise. And bounding boxes is one of the most preferred image annotation methods for object localization that can be used in image and videos having multiple types and class of objects making them recognizable into the crowd solving the computer vision problem.

Anolytics is one of the companies providing the image annotation service for machine learning training data needs. It is also well-versed in bounding boxes image annotation technique for different types of objects in images with accuracy. It can provide the best quality training data sets for autonomous vehicles, ecommeroce and robotics model perceptions making the computer vision more precise and usable in machine learning and AI. 

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