Data Scientist Course

According to an article on Harvard Business Review, Data Scientist is “sexiest job of the 21st century”. Even if this statement is unanimous today, the definition of a Data Scientist struggles to be universal.

The colossal amounts of data available to companies are mines of information: the challenge is to know how to extract its potential and draw useful conclusions from it thanks to Data Science. The Data Scientist’s main job is to implement algorithms based on data in order to respond to all types of problems ranging from stock optimization to weather prediction.

Based on the results from a survey we conducted in June 2021 among 30 companies from the CAC 40, the main benchmark index of the French stock market,  the four most important skills for a Data Scientist are in order of importance:

– Mastery of machine learning and mathematical statistics
– Programming and IT
– Fluency in written and oral communication
– Knowledge of the body of work

Although a Data Scientist who perfectly masters these four aspects can be difficult to find, an adequate training allows any future Data Scientist trainee to be up to date on these key points, in order to meet the expectations of recruiters and succeed in the Data Science career path.

For more information about the Data Scientist’s job, check out the video.

From the raw data, the Data Scientist develops algorithms with a view to responding to different needs and challenges such as:

– classification (e.g. spam or not spam)
– recommendation (e.g. services like Netflix or Amazon)
– grouping or clustering (without groups known beforehand)
– detection of anomalies (e.g. for bank fraud detection)
– text, audio, or image processing
– process automation (e.g. validation of bank card payments)
– segmentation (e.g. marketing based on demographic segments)
– optimization (e.g. risk management)
– forecasting (e.g. future profit based on different investments)

DataScientest makes you live a day in the shoes of a Data Scientist through this video.

An average workday for a Data Scientist can be divided in “work cycles”. The differents steps of this cycle are: 

– data acquisition, collection and storage
– identification of needs and goals (by asking the right questions)
– data processing and integration
– verification of the validity of the data with its qualification, deletion if necessary
– first data analysis (exploratory statistics) using data analysis tools
– choose one or more models and algorithms
– apply Data Science methods and techniques (machine learning, statistical modeling, AI)
– results measurement and improving

Data scientists use a variety of tools for different aspects of their work. Here’s a concise list:

Programming Languages : Python, R, SQL for data manipulation, statistical analysis, and machine learning.
Data Analysis Libraries: Pandas, NumPy, SciPy for Python; dplyr, ggplot2 for R.
Machine Learning Libraries: scikit-learn, TensorFlow, PyTorch, Keras for model building and training.
Data Visualization Tools: Matplotlib, Seaborn for Python; ggplot2 for R; Tableau, Power BI for interactive dashboards.
Big Data Technologies: Apache Hadoop, Spark for processing large datasets.
Database Management Systems: PostgreSQL, MySQL, MongoDB for data storage and retrieval.
Development Environments: Jupyter Notebooks, RStudio, Visual Studio Code for writing and testing code.
Version Control Systems: Git, GitHub, Bitbucket for code versioning and collaboration.
Cloud Services: AWS, Google Cloud, Azure for scalable computing resources.
Data Cleaning Tools: OpenRefine, Trifacta for preprocessing and cleaning data.
Statistical Software: SAS, SPSS for statistical analysis, especially in specific industries like healthcare or finance.

The Data Scientist curriculum consists of several modules:

– Programming in Python
– Data Visualization
– Machine Learning
– Advanced Machine Learning
– Big Data / Database
– Deep Learning
– Complex Systems and AI
 

All our courses were designed by our expert Data Scientists at DataScientest. DataScientest commits to exclusively utilizing in-house resources and expertise, ensuring that no external service providers are engaged, nor is content acquired through purchase. The content is produced through meticulous efforts and close partnerships with leading European corporations, which we consistently support in their daily operations.

The total duration of a course is 400 hours, including 280 hours of training and 120 hours for the project.

The courses are organized in sprints:

– First, the learning platform allows you to practice and validate your modules which will allow you to obtain your certifications at the end of the program
– Then, the project confirms the skills acquired, it must be completed, make a progress report and submit a deliverable to our teaching teams.
– In addition to the asynchronous courses, each sprint includes a videoconference Masterclass which allows you to take stock of the skills developed, to determine the objectives for the next sprint and to assimilate the concepts directly with your teachers.

Depending on the type of training chosen (bootcamp or continuing education), the training period on the platform takes place over one or more weeks.

If the content remains the same, the number of course hours differs depending on the format: 35 hours per week for bootcamps and 10 hours for continuing education

Throughout your training, and as your skills are developed, you will carry out a data science project. 

This project may come from our catalog, composed of various subjects, with technical business issues and using rich and complex data. You can also propose a personal project, as long as the data is accessible and our teaching team validates it.

It is an extremely effective way to move from theory to practice and to ensure that you apply the themes covered in class.

It is also a project highly appreciated by companies because it ensures the quality of the training and the knowledge acquired at the end of the Data Scientist training since the use of soft-skills is also very present.

– Ability to transmit information 
– Know how to present and popularize your work
– Know how to highlight data with interactive tools (Dashboard, Streamlit, etc.)
 
In short, it is a project that will require a real investment: at least a third of your time spent on training will be on this project .

The project is supervised by a DataScientest mentor who will regularly discuss with you to ensure your progress and to guide you.

According to the data managers of the largest CAC 40 groups, knowing how to communicate both orally and in writing is more important than mastering the core business of the company for a Data Scientist.

We have therefore taken this into account in our curriculum which also emphasizes soft-skills with:

– The written and oral defenses of the project, which allow these skills to be developed.
– Masterclasses dedicated to project management and the interpretation of results.
– Masterclasses on best practices in “data visualization” and on dedicated tools.
 
You will also have the opportunity to participate in CV workshops and career coaching via careers managers and the DataScientest HR team.

The Alumni community is a network of graduates who have completed their training with us. It serves as a platform for former students to stay connected, continue learning, and advance their careers. By joining the Alumni community, you can benefit from:

– Networking Opportunities: Connect with fellow professionals in your field to exchange ideas, share experiences, and build valuable relationships.
– Continuous Learning: Access exclusive resources, workshops, and events to stay updated on the latest industry trends and developments.
– Career Support: Receive information about job openings, career advancement opportunities, and professional development programs.
– Collaborative Projects: Engage in group initiatives, discussions, and projects that allow you to apply your skills and learn from others.
– Community Engagement: Participate in forums and social events that foster a sense of community and belonging among alumni.

Joining the Alumni community helps you maintain the connections you’ve made during your training and provides ongoing support for your professional growth.

In addition, as a B2B leader in Data Science training , DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. A fortiori, this confidence forges the recognition of one’s diplomas.

This training provides you with data analysis skills that are highly valuable in many professions beyond data-specific roles. By learning how to collect, interpret, and visualize data, you can enhance your ability to make informed decisions, identify trends, and solve problems in your current field. Whether you’re in marketing, finance, healthcare, education, or any other industry, the ability to leverage data effectively can lead to improved strategies, increased efficiency, and a competitive advantage in your profession.

Entry-Level Salary: After completing the Data Analyst training, entry-level positions typically offer salaries ranging from $50,000 to $65,000 per year in the United States. In Europe, entry-level salaries can range from €35,000 to €50,000 per year, depending on the country. These figures can vary based on factors such as the industry, company size, and geographic location.

Medium to Long Term: With several years of experience and a proven track record, Data Analysts can expect significant salary growth. In the medium to long term:

– Mid-Level Positions: Salaries can increase to $65,000 to $85,000 per year in the U.S., or €50,000 to €70,000 in Europe.
– Senior Roles: Senior Data Analysts or specialists may earn between $85,000 and $110,000 annually in the U.S., and €70,000 to €90,000 in Europe.
– Advanced Positions: Transitioning into roles such as Data Scientist, Data Engineer, or Analytics Manager can lead to salaries exceeding $110,000 or €90,000 per year.

Factors Influencing Salary:

– Location: Salaries are generally higher in major cities and tech hubs.
– Industry: Sectors like finance, healthcare, and tech often offer higher compensation.
– Skills and Certifications: Proficiency in advanced tools and obtaining certifications can enhance earning potential.
– Education and Experience: Higher degrees and extensive experience can lead to better opportunities and salaries.

We collaborate with a network of leading companies across various industries such as technology, finance, healthcare, and more. Our partner companies include both well-established corporations and innovative startups that are at the forefront of their fields.

How We Select Our Partners:
– Alignment with Our Mission: We choose companies that value data-driven approaches and innovation, aligning with the skills and knowledge we impart in our training programs.
– Industry Reputation: Partners are selected based on their standing in the industry and their commitment to excellence and ethical practices.
– Opportunities for Students: We prioritize companies that can offer meaningful opportunities to our graduates, such as internships, projects, or employment prospects.
– Collaborative Engagement: Companies that are willing to actively participate in our educational initiatives, guest lectures, and workshops are highly valued.

By carefully selecting our partners, we ensure that our training remains relevant to current industry needs and that our students have access to valuable resources and career opportunities upon completion of their programs.

To join our Data Scientist training program, having a bachelor level diploma in mathematics, statistics or science is recommended. However, regardless of any degree or diploma you have, our main requirement is that you can demonstrate the core competencies necessary to navigate our courses without significant obstacles. In addition to those hard skills, having good communication skills is preferable.

DataScientest stands out as the provider of hybrid training, blending 85% self-paced learning on our guided platform with 15% live masterclass sessions via videoconference. This unique approach ensures a balance between flexibility and structure, maintaining high standards without sacrificing either. Our pedagogical strategy is deliberately designed to foster motivated and effective learning.

To learn more about our training method, check this video.

Once you successfully complete your training, you will have acquired:

– The capacity to scrutinize company data, identifying key datasets for future extraction and processing.
– The skill to collect and examine pertinent data associated with the company’s production processes, sales, or customer information.
– The capability to construct predictive models aimed at forecasting trends and data evolutions relevant to the company’s operations.
 – The expertise to shape data analysis outcomes into actionable insights.

The evaluation process is designed to assess if the learner has attained the skills that are the primary focus of the program. The pedagogical team evaluates two key areas:

– Performance in professional scenarios.
– The final presentation of the project developed during these scenarios to a panel.

To achieve certification, the learner must successfully complete the professional scenarios and deliver a convincing final defense to the jury. A minimum score of 10 out of 20 is required in order the succeed.

Obviously ! And who better to provide support than our teachers, who also designed the program. They are available and attentive to all questions, whether theoretical or practical, and will be able to demonstrate pedagogy in their response. 

In addition, to ensure everyone’s completion and commitment, our teachers follow your progress closely . As soon as you stop logging in for an extended period, your cohort manager will hear from you: we won’t let you down!

Finally, our papers, exams and defenses are also corrected by hand by our panel of qualified teachers: everything is done so that everyone can progress effectively at their own pace. At DataScientest we are convinced that only personalized monitoring ensures quality learning!

1. Upon leaving your contact informations on our website, we’ll reach our as quickly as possible learn about your background and your carrer goals. Then, we will discuss about the Data Scientist training to determine, if it is suits your profile and your goals.
2. Next, you’ll complete a technical assessment that evaluates your understanding of key mathematical concepts such as probability, statistics, analysis, and algebra—subjects generally taught in the first two college semesters. This step ensures you meet the fundamental criteria necessary for comfortably engaging with the training.
3. After completing the test, an admissions team member will discuss your results with you, confirming your professional goals, motivation, and the fit of your educational plan.
4. With your project approved, you’ll move on to the enrollment phase. Our team will guide you through beginning your data science training, ensuring a comprehensive and personalized experience.

You can also finance your training by spreading your payments over 3, 6, 10 or 12 monthly installments, either to cover all the costs of the training or to cover the rest payable by the CPF.

Be that as it may, our teams are there to guide you through your administrative procedures for registering for the various funding aids.
To find all the financing possibilities, nothing could be simpler: we have created a page dedicated to the subject !

Yes, we provide support to help you in your job search after you complete your training with us. Our commitment to your success extends beyond the classroom, and we offer several resources to assist you in finding employment:

– Career Coaching: We offer personalized guidance on resume writing, cover letters, and optimizing your LinkedIn profile to attract potential employers.
– Interview Preparation: Gain confidence through mock interviews and receive feedback to improve your interview skills.
– Job Opportunities: Access exclusive job listings from our network of partner companies actively seeking candidates with your skill set.
– Networking Events: Participate in events, webinars, and workshops where you can connect with industry professionals and expand your professional network.
– Alumni Community: Join our Alumni network to stay connected with fellow graduates, share job leads, and continue learning through shared experiences.

Our goal is to provide you with the tools and support necessary to successfully navigate the job market and advance your career in the data industry.