Liora

Simplilearn is really great. the Curriculum is really good compared to other DataScience courses offered by others. There is a deep dive into every aspect of ML. the trainers went to great length to explain the the concepts. Trainers like  Shohini Kar & Nishant for Capstone Project, Mr Srinivas for Python, Mr Bhupendra for ML, and Maria for Big Data  all were really great ....Thank You all Read less

 Passées les premières certifications pour lesquelles l'examen en temps limité peut être source de stress et de contre-performance, le parcours est bien structuré, les exemples pertinents, et surtout le support est réactif (merci Daniel et Charles). Quelques coquilles et approximations orthographiques à corriger mais le fond est robuste  Read less

Pédagogique, ludique, la formation nous force à repousser nos limites. L'équilibre entre quantité et difficulté est maîtrisé, donnant envie de s'investir sans avoir peur d'être découragé.Depuis la formation, je passe la moitié de mon temps de travail à retranscrire mes modèles sur Python ! Read less

Cette formation très complète que j’ai commencé il y a maintenant 5 mois m’a permis de rapidement monter en compétence et de mettre en application concrète mes sujets appris lors de la formation. Grace à des exercices pratiques centrés sur des problématiques orientées métiers. Read less

Cette formation hybride m’a permis de monter en compétence rapidement. Les modules sont agréables et claires, la plateforme est intuitive et facile d’utilisation, l’accompagnement et le support sont personnalisés et continus ! Je recommande fortement DataScientest à ceux qui veulent se former efficacement au métiers des Data Science. Read less

 Cette formation a parfaitement répondu à mes attentes. Le format est idéal et permet une flexibilité bienvenue pour des emplois du temps chargés. Les cours sont très didactiques et adaptés à tous les niveaux. Je recommande cette formation qui sera, sans aucun doute, une plus-value pour la suite de ma carrière.  Read less

Les cours sont clairs et agréables à lire, et les mises en pratique permettent de bien appréhender les outils présentés. La formation permet de comprendre les enjeux (la puissance, les possibilités offertes) des technos présentées et de pouvoir ensuite les utiliser dans d'autres contextes que les jeux de données des cours ; les cours donnent également les clé pour pouvoir approfondir de son côté les notions qui m'intéressent le plus. Read less

Je suis ravie de cette expérience de formation. C’est une chance d’accéder à la fois à une formation de qualité et à un soutien aussi efficace. Pourtant mes compétences en Python sont limitées(je fais traditionnellement du R) mais j’ai fait un bond avec cette formation. Tout est expliqué ce qui permet de voir des progrès et de rester motivée. La formation fournit de solides bases que j’aurai le loisir d’approfondir par la suite.  Read less

I found the curriculum disorganized and rushed, with projects assigned way too early in the process. In a group project meant for 3-4 people, I was forced to work alone because one groupmate showed complete disinterest while another, despite the intermediate level of the course, not only lacked basic skills—unable even to use Microsoft Word—the first one resorted to verbally abusive behavior in front of the instructor and Datascientest did nothing against that. This reckless approach to grouping and timing not only undermined the learning experience but also created an environment that was both unproductive and unnecessarily stressful. It has been 5 months since I completed the course, I still do not have the diploma they promised. I was supposed to use that for job applications but I could not. I am now employed to a completely unrelated field to datascience and I may have wasted 4 months doing this course. I did not get any employment assistance from this course provider. 3-4 months of bootcamp will not make anyone emplyoeable in datascience... Read less

I just completed a very intensive Data Analyst bootcamp training course at DataScientest. The training is of high quality and the methodology is ideal for learning. It is "learning by doing". For each module, we learn by coding in Jupyter Notebooks or by creating Dashboards on various software: PowerBI, Looker Studio, Tableau. The core project carried out in groups during the training really allows us to put into practice all the knowledge and skills related to the training. It is a complete Data Science project that serves as a real experience! Read less

Course Attended: Machine Learning and MLOps Course

Pro:
1. Certification from University Pantheon Sorbonne
The course provides a certificate from the prestigious University Pantheon Sorbonne, offering a valuable credential for career growth. This is a major attraction for prospective students.

2. Flexible Learning Structure
The online format, combined with self-paced study materials, allows for flexibility—ideal for individuals managing multiple responsibilities.

3. Networking Opportunities
The course encourages professional networking, offering tips to help students connect with others in the fields of machine learning and MLOps. The shared learning environment fosters valuable professional relationships.

4. Constructive Exam Feedback
The course includes shared exam corrections, which provide insights into common mistakes and alternative approaches, enhancing the learning experience.

5. Includes a comprehensive practical project at the end, allowing students to apply their learning in a real-world, hands-on context.

Cons:
1. Course Content and Quality
- Localization Issues: The course materials frequently contain typos, grammatical errors, and poorly translated sections. Some content appears to be direct translations from French, which sometimes include untranslated French text, making comprehension difficult for non-French speakers.
- Incomplete Curriculum: Critical topics, like the Central Limit Theorem in statistics, are missing from the curriculum. Such gaps leave students without essential foundational knowledge.
- Limited Practice Opportunities: There is a lack of preparatory exercises for exams, which limits the ability of students to properly practice and feel prepared.
- Inconsistent Language Delivery: Despite requesting English versions of course materials twice, some lectures and notes were still delivered in French. This inconsistency in language delivery demonstrates poor responsiveness and engagement.
- Poor English Proficiency: Some instructors have limited proficiency in English, making their explanations unclear and hard to follow.

2. Exam Structure and Support
- Short Exam Durations: Exam times are unrealistically brief, which prevents students from adequately demonstrating their knowledge.
- Generic Feedback: Feedback after exams is vague and lacks actionable details, hindering students from clearly identifying areas for improvement.
- No Exam Preparation Resources: The absence of targeted preparatory exercises adds unnecessary difficulty to the exam process. Students are allowed to take the exam multiple times, enabling them to access the questions online, review them after an initial failed attempt, and then retake the exam with prior knowledge of the questions. This practice, known and even encouraged by the instructors, significantly undermines the integrity and effectiveness of the evaluation process. 

3. Scheduling and Timing Issues
- Frequent Delays: Important meetings, including introductory sessions, often start 5-10 minutes late, which suggests poor time management and professionalism.
- Inconvenient Timings: Some live sessions are scheduled late in the evening, sometimes extending until 7:30 PM, which can be challenging for students with other commitments.

4. Technical and Administrative Shortcomings
- Technical Disruptions: Frequent technical issues disrupt the learning process, reducing the overall value of the course.
- Low-Quality Certificates: Intermediate certificates are poorly formatted, with overlapping text and QR codes, making them appear unprofessional and diminishing their value.

5. Pricing and Transparency Concerns
Pricing Discrepancies: Applicants using Germany's Bildungsgutschein are charged approximately €4,000 more than other students. This difference is attributed to supposed "extras" that do not justify the cost.

Questionable Extras:
- Extended Platform Access: Marketed as a benefit, but this should be standard for a course costing €13,500.
- AWS Course Access: The AWS courses included are publicly available for free, adding no real additional value.
- Integrity Concerns: The inflated pricing and lack of transparency raise serious questions about the program's integrity, especially given the marginal value of the supposed "extras."
- Ignored Price Adjustment Requests: Despite requesting a price adjustment to meet the €10,000 limit set by Agentur für Arbeit, the provider was unwilling to accommodate. The insistence on maintaining inflated pricing for superficial "benefits" like extended platform access and free AWS courses reflects a disregard for students' needs.



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The Data Analyst continue is a 6 months, part-time and requires a prerequisite of a diploma or a level 6 title (equivalent to a bachelors degree). You will learn languages, such as Python, NumPy, Pandas, SQL, PySpark, CSS, HTML, PowerBi, Tableau, web scraping, and text mining. We also learn the art of storytelling and dashboard creation as well as machine learning concepts. Read less