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Business Analyst, part-time (15 months) (Titre RNCP)

by DataScientest

Practical course - 25d - 175h00 - Ref. 3AZ
Price : 11990 € E.T.

Business Analyst, part-time (15 months) (Titre RNCP)

by DataScientest

View the programme in french



Become an expert in data analysis to improve processes, products or services. A Business Analyst (BA) is a professional who plays a key role in organizations, acting as a bridge between technical teams and business departments. This certification course is delivered remotely in a hybrid format combining synchronous exchanges with an expert trainer, practical exercises and e-learning modules. Based on the Learning By Doing pedagogy, you will carry out a red thread project in a team to put your knowledge into practice. When you enroll, you will be assigned to one of the Datascientest promotions. At the end of the course, you'll receive a Business Analyst certificate from Mines Paris- PSL Executive, as well as the RNCP level 7 certification "Manager en data marketing" issued by INSEEC MSc and registered with the RNCP under n°RNCP39591. Contact us now to find out about upcoming dates!


Catalog
Custom

Online course

Ref. 3AZ
  175h00
11990 € E.T.
Coffee breaks and
Complimentary lunches
Language : EN
This course is also available in English.




Become an expert in data analysis to improve processes, products or services. A Business Analyst (BA) is a professional who plays a key role in organizations, acting as a bridge between technical teams and business departments. This certification course is delivered remotely in a hybrid format combining synchronous exchanges with an expert trainer, practical exercises and e-learning modules. Based on the Learning By Doing pedagogy, you will carry out a red thread project in a team to put your knowledge into practice. When you enroll, you will be assigned to one of the Datascientest promotions. At the end of the course, you'll receive a Business Analyst certificate from Mines Paris- PSL Executive, as well as the RNCP level 7 certification "Manager en data marketing" issued by INSEEC MSc and registered with the RNCP under n°RNCP39591. Contact us now to find out about upcoming dates!


Teaching objectives
At the end of the training, the participant will be able to:
Design and deploy the company's data-driven marketing strategy.
Implement data projects in agile mode to support transformation.
Manage the company's data collection, cleansing and analysis strategy.
Add value to data, make recommendations and support decision-making.

Intended audience
Anyone with an appetite for data who wants to retrain or upgrade their skills.

Prerequisites
Un diplôme ou un titre de niveau bac+3 ainsi qu'un bon niveau en mathématiques.
Pour les candidats ne présentant pas le niveau de qualification requis, une dérogation est possible sur dossier.

Certification
Pour clôturer la formation, l’équipe pédagogique évaluera le projet fil rouge de l’apprenant à l’aide d’un rapport écrit et d’une soutenance à distance. En complément la réalisation d’une période en entreprise de 132 jours minimum est obligatoire pour valider le titre. La validation des compétences développées au cours de la formation Business Analyst vous permettra d’obtenir : • Un certificat de l'École des Mine Paris - PSL Executive Education • La certification RNCP de niveau 7 “Manager en data marketing” délivrée par l’INSEEC MSc et enregistrée au RNCP en date du 02- 10-2024 sous le n°RNCP39591.

Practical details
Digital activities
Online courses and exercises, group masterclasses, question/answer sessions, support classes, e-mail coaching, red thread projects, individualized career coaching, social learning.
Mentoring
An expert trainer accompanies learners throughout their training. He or she regularly discusses the learner's project and provides individual mentoring. Several trainers also lead the various masterclasses (group classes) and answer learners' questions at any time from a dedicated forum. In addition, numerous question-and-answer sessions can be organized to help learners.
Pedagogy and practice
Upon registration, the learner is assigned to a class (dates to be defined at the time of registration) and receives a training schedule. The training program is divided into "Sprint" sessions lasting several weeks on a dedicated theme. Each week, the learner is invited to a time of exchange with the trainer, in the form of a masterclass (group class) or mentoring sessions (individual). For 80% of the time, the learner works independently on the teaching platform. All modules include practical exercises to put into practice the concepts developed in class. Learners are also required to work in pairs or trios on a common theme throughout the course. This will enable them to develop and gain recognition for their skills. In addition, themed events and workshops are regularly offered to enable learners to discover the latest innovations in Data Science. In order to follow the course effectively, we estimate the time required at between 10 and 12 hours per week.

Course schedule

1
Upcoming session dates

  • January 2026: Start date 01/13/26
  • September 2026: Start date 08/09/26

2
Introduction to Data Marketing

  • Data in business: data professions, data concepts and pipelines, data-driven decision-making.
  • Forms of data consumption: DataViz, data storytelling, data acculturation (AI, machine learning...)
  • Data Architecture fundamentals: data structure and type, databases, OLTP, OLAP, storage, ELT vs. ETL.

3
AI, growth hacking & marketing automation

  • AI & Growth Hacking: scraping and automation, ChatGPT, AI and automation.
  • Getting to grips with Copilot and Dall-E: tools, Bot/Agent distinction, best practices in image generation.
  • AI regulations and Inclusivity: RGPD, IA Act, data security and accessibility.
  • Voiceflow, chatbot creation (optional): interface and functionalities, chatbot example, practical application.
  • CRM & marketing automation with Hubspot: data centralization and organization, marketing automation.
  • Automating content creation with Zapier: interface exploration, workflows and social networks.
  • Automated content creation with Make: content adapted for people with disabilities.

4
Tracking & data analysis

  • Google Analytics 4: Getting started, difference with Universal Analytics, main reports, attribution-contribution.
  • Tracking basics: HTML / CSS introduction, tracking, pixel and matomo.

5
Main sources of acquisition

  • The keys to traffic acquisition: SEO, Paid Media, Inbound Marketing.
  • Social media: advertising on social networks, content strategy on social networks.
  • SEA with Google Ads: Paid Search Google Ads.
  • Marketing strategy: strategic diagnosis, objectives and KPIs, plan and evaluation, strategic choices.

6
Data Visualization & Business Intelligence

  • Looker Studio: connection to data sources, dashboard, advanced features.
  • Power BI (optional): Business Intelligence, Power BI introduction, Power Query, DAX language, visualization, security.

7
Web design and regulations

  • RGPD and ethics: concept, anonymization and pseudonymization.
  • UX/UI Design: wireframes, interactive mock-ups and prototypes, best practices.

8
Programming with Python

  • Python fundamentals: variables, lists and tuples, operators and structures, loops, types, functions, classes.
  • NumPy: NumPy Array, matrix operations, statistical indicator and applications.
  • Pandas: dataset exploration, Data Cleaning, Data Processing.

9
Data Visualization

  • Matplotlib: graph types, graph customization.
  • Matplotlib add-on (optional): advanced graphics, figures with classes and objects.
  • Seaborn: distribution, statistical and multivariate analysis.
  • Bokeh (optional): interactive graphics that can be integrated into Web pages.
  • Plotly (optional): interactive graphics that can be integrated into Web pages.

10
Database / Big Data

  • SQL language: queries, join types, nested queries.
  • Data API fundamentals: using an API, formatting an API.
  • Fundamental theory of data integration (optional): relational databases, modeling, schemas.

11
Business Intelligence

  • Power BI: connection to data sources, Power Query, modeling, DAX language, in-depth dashboards.
  • Google Sheets & Excel: basic functions, charts.
  • Business Intelligence: introduction to BI.
  • Looker Studio (optional): connection to data sources with Looker Studio, dashboard.
  • Table (optional): connection to data sources, formatting, advanced functions.

12
Advanced data visualization

  • The art of storytelling: dataset analysis and interpretation.
  • Streamlit: webapp with Python, interactive graphs and widgets, Data Storytelling API.
  • Dash (optional): design dashboards with interactive graphics, filters and drop-down menus.

13
Advanced databases

  • Advanced SQL: standard and exotic joins, aggregation functions, downstream selection.
  • Cloud basics for the data analyst: basic concepts, the role of data analysis in the cloud.
  • Snowflake (optional): Data Warehousing, SQL data storage and analysis.

14
Web & Text Analytics

  • Text mining: regular expressions, text data, wordclouds, sentiment analysis.
  • Prompt Engineering: generative AI, prompts and best practices, workflow and automation.
  • Web Analytics: site performance, customized reports.
  • Web Scraping with BeautifulSoup (optional): web language (HTML, CSS), web content extraction, Google scrapping.

15
Supervised Machine Learning

  • Machine Learning Data Analyst: concepts, Scikit-Learn API, linear models, tree models, methodology.
  • Classification algorithms and methodology with Scikit-Learn (optional): boosting and bagging, models.

16
Introduction to Data Product Management

  • Understand the role of a DPM: business context, background, Product and Project, responsibilities.
  • Agile method: introduction, tools and methods, application to Data, impact within the company.
  • The tasks of a DPM: understanding a need, proposing a solution, monitoring success, alternatives.
  • Project management: issues, project management models, change management.

17
Data Governance

  • Case studies: customer needs, digital solution creation, strategic errors, solution proposals.
  • RGPD and Ethics: Data context, anonymization and pseudonymization.

18
Project Management

  • Final project: product strategy, product discovery, product delivery.
PARTICIPANTS
Anyone with an appetite for data who wants to retrain or upgrade their skills.

PREREQUISITES
Un diplôme ou un titre de niveau bac+3 ainsi qu'un bon niveau en mathématiques.
Pour les candidats ne présentant pas le niveau de qualification requis, une dérogation est possible sur dossier.

TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.

ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.

TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.

TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.

ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.