Choosing a Data Engineering course is about more than learning SQL, Python, or big data tools. Students and working professionals also want to know whether the learning is practical, whether instructors can explain difficult concepts clearly, and whether mentorship can help with career preparation.
The alumni experiences shared by Bosscoder Academy provide a useful perspective on these questions.
The four stories featured here come from learners with different starting points. One was already working in Data Science, one was a working professional moving into Data Engineering, one transitioned from Biotechnology to Data Analytics, and another moved from self-learning into an AI/ML career.
Their experiences show the impact that structured learning, problem solving, live classes, 1:1 mentorship, and interview prep can have on one’s career development.
Important Note: Individual career achievements and salary increases of alumni given below are not guaranteed and they depend on personal experience, skills, interview results, job position, etc.
What Does the Bosscoder Data Curriculum Cover?
The curriculum that Bosscoder Academy offers within its data-focused courses include knowledge relevant to various occupations in fields of Data Engineering, Data Analytics, Data Science, and Machine Learning.
From the experience of the alumnus provided, the learning process includes topics such as:
- SQL and Python fundamentals
- Data analysis and problem-solving
- Machine Learning principles and algorithms
- Data pipeline design
- Concepts of big data
- SQL queries optimization
- Real-world data architecture
- Technical interview preparation
- Mentorship and career guidance
Curriculum may vary from one program to another, so the learners need to analyse the curriculum of the particular course they are interested in.
The learning pathway may be visualized as follows:
Fundamentals → Technical Skills → Problem Solving → Mentorship→ Interviewing → Career Transition
1. Padmanabh Manikandan: From Data Science to ML Engineer
This success story can be inspiring for professionals who are currently in the data science field but want to improve themselves and move to data engineering domain.
Padmanabh had been working at a startup in Data Science for about two and a half years before joining Bosscoder. His aim was to gain more skills and move to a better company.
His journey:
→ 2.5+ Years in Data Science
→ Joined Bosscoder Academy Data Engineer Program
→ Learned Python & Machine Learning
→ Daily Practice & 1:1 Mentorship
→ ML Engineer at Maxhome.ai
According to Padmanabh, the program content was interesting and helpful for him as far as his career transition was concerned. More specifically, he learned Data Science basics, Python, and Machine Learning during the program.
He also thanked Mr. Akash, who is an instructor, for his lectures and explanations.
One of the most important things in his review is his way of practicing. Padmanabh explained that he solved problems on a daily basis, which boosted his confidence.
He found the mentorship guidance helpful, although he had less number of mentorship sessions.
The result was a switch from his startup life to Maxhome.ai as Machine Learning Engineer, with a reported 225% salary hike.
2. Swetha: Learning Data Engineering Skills While Working
Swetha's case will be particularly useful for working professionals who are looking into Data Engineering courses while continuing their careers.
Her review is mainly focused on the practical nature of the classes and how the instructors delivered Data Engineering concepts in understandable ways.
Her journey:
→ Working Professional
→ Joined Bosscoder Academy
→ Learned Data Engineering & SQL
→ Practiced Data Pipelines & Big Data
→ Learned Real-World Architecture
→ Became a Data Engineer
Specifically, Swetha mentions that she learned:
- How to design data pipelines
- To deal with big data
- How to optimize SQL queries
- The real-world architecture
- Hands-on problem-solving
One of the most important things that Swetha highlighted in her review is the difference between theoretical knowledge and its practical application.
Swetha speaks of the classes as being highly interactive and says that the professors helped her understand even the most complex topics.
However, she did admit that combining the course with her day-to-day job was difficult. Still, the quality of the curriculum and the teaching made her efforts worthwhile.
Swetha switched from IQVIA to Mimecast where she works as a Data Engineer and also has a reported salary increase by 118.27%.
3. Mohit Soni: Biotechnology to Data Analyst
Mohit's experience differs from the other alumnus because he studied Biotechnology.
While studying Biotechnology in university, he realized that he wants to shift to IT, specifically Data area. His goal was to gain the necessary skills and join the industry.
His transition:
→ Biotechnology Background
→ Career Switch to Data
→ Learned SQL & Python
→ Mentorship & Interview Preparation
→ Data Analyst at Affine Analytics
In particular, Mohit mentions the importance of SQL and Python during his studies.
For example, he found Megha Roy's tutorials on complicated SQL subjects useful.
Additionally, the work of his mentor, Mr. Ankit Dutta, also contributed much since he helped Mohit to understand what to focus on while preparing for interviews.
As a result, Mohit Soni managed to switch his career path and became a Data Analyst at Affine Analytics, receiving 100% salary hike.
What's important to note is:
- Academic non-IT background
- Learning SQL and Python
- Instructor guidance
- 1:1 Mentorship
- Interview preparation
- Becoming a Data Analyst
The experience of Mohit will be particularly helpful to those learners or who want to switch career who are doubtful about their ability to transition into the data domain with a non-IT academic background.
4. Sanat Kumar: Adding Structure to Data Engineering Interview Preparation
The experience of Sanat is a bit different than that of the rest of the alumni, where even before joining Bosscoder Academy, he was preparing himself for becoming a Data Engineer and had attended various interviews for this.
He did not want to start from zero, but instead make his preparations more structured and organized and have some direction in them.
Learning Journey of Sanat:
→ Data Engineering Preparation
→ Joining Bosscoder Academy
→ Concept Revision Through Live Sessions
→ Mock Interviews & Mentorship
→ Doubt Support & Practice
→ Data Engineer at PwC
Sanat found the live sessions helpful for organizing his revision process. In addition to this, he found the importance of 1:1 mentorship and mock interview sessions very helpful because these sessions provided him with a realistic environment for practice.
Doubt support was also an important component of his learning process. He was able to get help whenever he used to get stuck while practicing.
More important than anything else, according to Sanat, Bosscoder Academy has provided him with direction and consistency on top of his existing preparation.
His career transition from Cognizant to PwC as a Data Engineer with an increase in salary of 121%.
What Stands Out:
- Already preparing for Data Engineer interviews
- Needed more structure and guidance
- Concept revision
- Practice interview sessions
- Guidance and doubts clarification
- Became Data Engineer at PwC
What Do These Bosscoder Alumni Reviews Tell Us?
These four alumni reviews are from various backgrounds and career levels. However, there are a few common points that one may draw from them. These points provide a practical understanding of Bosscoder Data Engineering and data-oriented learning overall.
1. Practical Skills Over Just Theory
These reviews mention practical skills needed for working with data, such as:
- SQL & Python
- Machine Learning
- Data Pipelines & Big Data
- Real-world Architecture
- Technical Problem-solving
Thus, practical approach may be useful for understanding how things work in practice rather than what they are.
In this way, learning something about a particular skill becomes easier because you can see how this skill is applied in real-world data work.
2. Live Classes for Better Understanding
There were many alumni who appreciated the live interactive classes.
Learning process can be explained as:
Learning → Understand with Examples → Doubts → Practice
Alumni have specifically pointed out how teachers helped with learning different topics such as SQL, Python, Machine Learning, and Data Engineering concepts.
3. Mentorship and Interview Support
Mentorship was another point which kept coming up from reviews.
- Focus on key topics
- Resolve technical questions
- Resume review
- Mock interviews for practice
- Confidence building for interviews
For career switchers and working professionals, this guidance can provide more direction during preparation.
4. Consistent Practice Builds Confidence
Learning is an ongoing process and does not end at lectures alone as per the alumni’s experience.
Learning → Practice → Identify Weaknesses → Improve → Repeat
According to Padmanabh, practicing questions is essential. On the other hand, Sanat stressed on the need for mock interviews and doubt clearing sessions.
While a Data Engineering course may offer the plan, consistent practice will help in developing the skills required.
The Common Thread
From all these four experiences, the learning process can be summarized as follows:
Structured Learning -> Practical Skills -> Mentorship -> Continuous Practice -> Mock Interviews -> Career Growth
All four alumni profiled here came from various backgrounds, had different goals, and were at various stages in their careers. The career outcomes are personal experiences of each alumni and cannot be taken to be the promise of a particular job or placement.
In evaluating the Bosscoder Data Engineering course, this would make a great first step in understanding the curriculum, the style of learning and mentorship provided in order to determine if it will suit your career objective.
Frequently Asked Questions About Bosscoder Data Program
Q1. Is Bosscoder Academy good for Data Engineering?
According to the experiences shared by the alumni in this blog, Bosscoder Academy is a worthy choice for anyone interested in taking up structured and practical Data Engineering learning. Alumni have highlighted live classes, Data Engineering concepts, SQL, data pipelines, big data, real-world architecture, mentorship, and interview preparation.
Q2. What does the Bosscoder Academy Data Engineering curriculum cover?
The data-oriented curriculum includes topics including SQL, Python, data pipelines, big data, SQL optimization, real-world data architecture, problem-solving skills and technical interview preparation.
Q3. Does Bosscoder provide mentorship and interview preparation?
Yes, according to alumni feedback, Bosscoder provides 1:1 mentorship, mock interviews, resume prep, preparation for technical questions, and doubt clearing. For example, Sanat kumar has found the mock interviews and mentorship helpful in making the interview practice more realistic.
Q4. Is the Bosscoder Data Engineering course suitable for working professionals?
Yes, the course can be a relevant choice for working professionals interested in gaining a more organized approach towards studying Data Engineering. Swetha shared her experience of combining this course with work and mentioned the importance of live classes, structured learning, problem solving and architecture practices.
Q5. Can Bosscoder help with a career transition into the data field?
There have been alumni who have come from different professions and at different stages of their career to learn data-related skills. For example, Mohit, who had a background in Biotechnology, was employed as a Data Analyst. There were also some alumni who shifted to Data Engineering and Machine Learning jobs.
