Course Review – Data Science and Machine Learning by Coding Ninjas

The program offers one of the widest range of services that a candidate might require at the start of their professional journey. The majority of the program emphasizes the application of the knowledge of Python and Machine Learning concepts in real-world challenges. Each project is designed to furnish a new skill for the candidate’s development. Data Science and Machine Learning are increasingly becoming popular courses for those who are interested in coding.

The curriculum aims at building a strong foundation in coding, web development, mobile development, and machine learning. It also offers an opportunity to showcase your learnings on public domains with guidance from the faculty of experts including members of the Stanford Alumni. The program understands the need for non-technical support and the importance of soft-skills development which it fulfills by laying out end-to-end support with GitHub profile building, aptitude development tips, and interview preparation. Read more about Data Science And Machine Learning Courses and Certifications. Here is a review of the Coding Ninjas machine learning and data science certification course.

Highlights

Key Features

The Data Science and Machine Learning program provides the flexibility of learning by presenting recorded lectures covering topics like Statistics, Neural Networks, and Trees which can be managed along with current professional commitments. It also provides a live webinar with experts in a month to gain insights regarding the challenges associated with complex concepts. The 12 projects are incorporated into the program by keeping in mind the latest trends in the Data Science sector. These projects are updated and designed with state-of-the-art technologies every three months in order to expose the understudies to contemporary scenarios. The program also offers personalized guidance sessions with industry mentors and various workshops for polishing one’s resume and networking skills. The carefully curated database for placement preparation includes more than 100 interview problems and the DSA-based product company's Mock Test series for furnishing maximum placement preparation opportunities for each candidate to avail of.

Content coverage: The program covers all chief aspects of data collection, data cleaning, machine learning, to deployment. The course promises 12 projects and 70+ hours of recorded video learning content.

Mode of Learning: The course is completely online mostly in the form of recorded content. It comes with four choices for students to pick from:

• Basic plan for nine months, 12+ projects, 70+ hours of recorded learning, and personalized doubt-clearing sessions.

• Standard plan for 15 months, 12+ projects, 130+ hours of recorded learning, and 300+ Problems.

• Pro plan for 11 months, 12+ projects, 70+ hours of recorded learning, and 100+ Problems.

• Premium plan for 17 months, 12+ projects, 130+ hours of recorded learning, and 400+ Problems.

Target Audience: The program is designed for both beginners and experts who would like to excel in the field of Data Science and Machine Learning.

Learning support: The program covers problem-solving sessions with a dedicated technical assistant system. The live webinars taught by industry experts also solve queries on the go.

Price Aid: The program provides a spectrum of payment and no-cost EMI options. Additionally, it provides a 30% early bird discount and an option for students to avail 100% scholarships. It also provides a guarantee of the money back within 7 days.

College pedigree: The certification is directly provided by Coding Ninjas.

Also Read:

• Data Science vs Machine Learning
• Free Data Science and Machine Learning Courses and Certifications

What makes the course unique

The certification forks out the strengths of each candidate by providing exposure to the assorted fields of Data Science. The curriculum strategically teaches the fundamentals of programming to provide clarity associated with each tool and steps up to teach the application of the basic ideas. A distinctive service furnished by the program is the possibility of pausing the course at any time of the program and resuming it from the same stage. It purveys lifetime access to curated jobs and internships for placements with the help of their placement coach. It also extends the orbit of soft-skill improvement opportunities with a team of experts for holistic support. Here is a small Coding Ninjas machine learning course review by an expert.

Popular Providers Offering Data Science and Machine Learning Courses & Certifications

Course Offering

The following points cover the chief offerings by the certification.

• This unique opportunity to pause the course duration in accordance with personal flexibility of time.

• Hands-on learning over essential tools including open-source software such as Jupyter Notebook, Pandas Library, and Tableau for visualization.

• Personalized industry mentors and placement coaches to provide guidance through each round of placement.

Key Discussion Points

Value Addition: Deep insights into the workings of the Data-science industry, strategically designed projects, interview preparation, personalized industry mentors, and faculty training from Stanford alumni.

Points to debate: There is a lack of peer interaction groups for doubt-clearing or networking options. However, the live webinar is an interactive experience that attempts to clarify most concepts in a collaborative method.

Career Options

Data Scientist: A Data Scientist is responsible for developing an end-to-end solution that will help an organization in data-backed decision-making and business intelligence. They work on the entire data pipeline and data strategy to first standardize data and leverage algorithms to solve complex use cases. These use cases are dynamic in nature and a Data Scientist must be cognizant of the fact that they must build solutions that are scalable and offer continuous learning and adaptation.

Data Engineer: Data Engineers work with Data Scientists to clean, categorize, and standardize data at an organizational level. As the number of touchpoints and the amount of collected data grows, so does the need to standardize and store data in an efficient manner. This is where a Data Engineer plays the central role in setting up the entire data storage and standardization techniques to enable data-backed decision-making.

ML Engineer: A Machine Learning Engineer is responsible for working with ML algorithms and industry data, in order to solve enterprise-wide use cases. Their profile often overlaps with that of a Data Scientist. They must understand and analyze the domain in question, along with the suitable algorithms that can be used to solve the use cases. They should be comfortable with popular open-source tools, languages, and libraries such as Jupyter Notebook, Python, Keras, and Pandas. Their profile plays a central role in developing data solutions. Hence, it is imperative for an ML Engineer to understand other complementary technologies as well.