Build a Career in Data Science

Uplatz offers this comprehensive course on Build a Career in Data Science. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

Data science exists at the intersection of a number of genuinely technical elements, from analysis to statistics to programming to machine learning. So it’s easy to be diverted into thinking that it’s a purely technical domain.

But there’s much more to data science and analytics than solving technical problems. And because of that, there’s much more to the data science job search than coding challenges and Kaggle competitions. Landing a job or a promotion as a data scientist calls on a ton of career skills and soft skills that many people don’t spend nearly enough time honing.

Big companies are often better placed to offer things like on-the-job mentorship, whereas smaller ones might struggle more with that aspect of on boarding new employees. For that reason, Emily suggests looking at big companies if you’re kick-starting your career, to ensure you have the support you need to grow into the role.

Data needs to be collected from multiple sources, and analyzed. Data analysis and visualization has to be performed on the data to get valuable insights into the data. Machine learning tools are deployed to build predictive models that transform the raw data into actionable information. Knowledge Representation and Artificial Intelligence algorithms are creating intelligent machines capable of solving complex problems. Trending technologies like cloud computing, blockchain, quantum computing are transforming data science. Effective data architecture needs to be designed for useful storage and retrieval.

On the other hand, the Data scientist has more experience, and can use his or her core competencies for problem-solving in diverse domains such as finance, insurance, retail, and healthcare. The data scientist is able to explore data, formulate problem statements in line with the business model, and engineer effective end-to-end solutions. In addition to computational and empirical skills, the most important function of a data scientist is to be able to identify problems where data science could be used, enhance human decision making through data driven insights.

This is a comprehensive self-paced course on "Build a Career in Data Science" provided by Uplatz.

• Build a Career in Data Science - all lectures 2:21:44

  • 1: How to learn Data Science - part 1 25:08
  • 2: How to learn Data Science - part 2 18:42
  • 3: Introduction to Data Science 54:04
  • 4: Data Science Learning Path 43:50

Since data science is a high-growth, in-demand career field with strong job prospects, it’s a good time to explore whether becoming a data scientist is the right next career for you.

The great news is, you don’t need prior experience to become a data scientist. There are plenty of ways to acquire a data science skillset on your own.

Who is a data scientist?

Data scientists collect and clean large amounts of data, maintain easy-to-use dashboards and databases, interpret data to solve problems and run experiments, build algorithms, and present data to stakeholders in attractive visualizations.

Today there are many reasons to pursue a career in data science: a high salary, stable and growing job market, and exciting problems to solve across diverse industries.

Whether or not data science is hard really depends on your background and whether you enjoy working with numbers and data. While data scientists do not need as much software engineering or machine learning as data engineers, you will need to learn how to code in order to build predictive models.

Data science has a steep learning curve, involving tough problems, a large amount of data, technical expertise, and domain knowledge, but luckily there are many free online resources to help you get started as an entry-level data scientist. Hopefully, you enjoy learning because data scientists are constantly upskilling and learning new technologies.

Key skill-set of Data Scientists

• Statistical methods and probability theory
• Probability distributions
• Multivariable calculus
• Linear algebra
• Hypothesis testing
• Statistical modeling and fitting
• Data summaries and descriptive statistics
• Regression analysis
• Bayesian thinking and modeling
• Markov chains

Compared to other career fields, data science is more about what you know and how well you can prove your relevant skills and less about the prestige of your alma mater. The skill-based interview process tends to level the playing field for people coming from different backgrounds.

Once you have a solid foundation with math, you can begin to pick up a few of the must-know programming languages for aspiring data scientists: SQL, R, Python, and SAS.

Data analysts manage data collection and identify dataset trends.

• Data scientists not only interpret data but also apply skills in coding and mathematical modeling
• Data analyst positions can be easier to break into as a first job and can be a great launchpad to a data science career

Top profiles of a Data Scientist include:

1. Data Scientist

The data scientist is one who has been working in diverse domains. The data scientist is able to define the problem statement, project objectives in line with the business goals. They help identify patterns and trends using artificial intelligence, machine learning and make predictions based on data. They are required to have a strong background in the related subjects of artificial intelligence, machine learning, statistics and data engineering.

2. Data Analyst

Typically, the data analyst works with the business and management team to establish the project objectives and business needs. They facilitate the collection of relevant data and exploration of the data. They transform the data and analyze it to interpret patterns and trends. They also aid in presenting the patterns and visualizing the data to help the team translate the patterns into actionable items.

They must have exceptional interpersonal skills, with technical skills like programming, database management, data analytics and data visualization tools. Expertise in Machine learning and a thorough understanding of cloud platforms, such as Azure, IBM, and Google is expected. Business analysts specialize in business intelligence and work with business models and their relevant technology.

They need to have a good understanding of business, finance and as well as IT technologies such as data modeling, data visualization tools etc. Similarly, Financial Analysts are highly specialized in the areas of finance, and work on building systematic trading models, strategies and trading signals.

3. Data Engineer

Traditionally, organizations hire, database administrators to administer and manage the data on a daily basis. They are responsible for maintaining the integrity and performance of the organization's databases, and ensuring the security of the organization's data. They are required to have knowledge of traditional relational databases, disaster recovery and database backup procedures, and familiarity with reporting tools.

The Data engineer, on the other hand is responsible for developing and maintaining scalable data pipelines and building APIs to support the data repositories. The data models have become diverse in nature and knowledge in data formats, big data technologies to populate data models has become a necessity.

4. Enterprise Data Architect

Data architects and stewards provide data management services for the enterprise at a strategic level while ensuring data quality, accessibility, and security. The Enterprise Data architects are the ones who create blueprints for data management, pipelines and its repositories at a strategic level.

They build and maintain an organization's database by identifying the layers of technology, the performance and database size requirements. They also work with the data engineers and administrators to ensure the strategic use of the data while ensuring performance, privacy and security.

The top technology trends that could affect the future are artificial intelligence and quantum computers. Quantum computers have processing units that could potentially support the computational requirements of the next generation of applications. AI enabled Biometrics allows a person to be identified and authenticated based on recognizable and verifiable data such as voice, iris, fingerprint and face.

The Biometrics based technology such as DNA matching, Retina recognition, voice recognition, etc. will transform security and authentication systems. With the help of AI and ML, computer vision enabled technology is able to accurately identify and classify objects in images and videos. The proliferation of IoT, smart personal devices and wearable devices will further spawn the application of AI and ML in transportation, healthcare and medical sciences.

The use of Natural language processing methods is important to ensure human commands result in automation.

The use of artificial intelligence in self-driving vehicles can help in reducing collisions and the burden on drivers.

Everyone

Passion to learn and succeed!

• Data Scientist
• Senior Data Scientist
• Data Engineer
• Data Analyst
• Data Architect
• Machine Learning Engineer
• Technology Consultant

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Certificate of completion

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included