Data Scientist

Data scienceData Science is a broad field of study about data systems and processes aimed at maintaining data sets and deriving meaning from them. Data scientists use tools, applications, principles, and algorithms to make sense of random data clusters. Since almost all kinds of organizations generate exponential amounts of data worldwide, monitoring and storing this data becomes difficult. Data Science focuses on data modeling and warehousing to track the ever-growing data set. The information extracted through data Science applications is used to guide business processes and reach organizational goals.There may be overlaps in these domains now and then, but each of these three terms has unique uses.  A business analyst profile combines a little bit of both to help companies make data-driven decisions.  Artificial intelligence AI, a rather hackneyed tech term used frequently in our popular culture – has come to be associated only with futuristic-looking robots and a machine-dominated world. However, in reality, Artificial Intelligence is far from that.Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the correct information and self-correction is crucial. AI experts rely on deep learning and natural language processing to help machines identify patterns and inferences.Machine learning Machine Learning is a subsection of Artificial intelligence that devices mean by which systems can automatically learn and improve from experience. This particular wing of AI aims to equip machines with independent learning techniques so that they don’t have to be programmed. This is the difference between AI and Machine Learning.

Congratulations on completing a Data Science course! Pursuing a career at Google is an exciting goal. To increase your chances of landing a job at Google, follow these steps:Resume and Online Presence: Update your resume to highlight your Data Science course, skills, and any relevant projects or experiences. Ensure your LinkedIn profile is complete and showcases your skills and achievements.Research: Thoroughly research Google, its culture, values, and the specific role you are interested in. Understand the skills and qualifications they look for in candidates.Job Search: Visit Google's official careers website (careers.google.com) to search and apply for suitable Data Science roles. Use keywords like "Data Science, " "Data Analyst, " or "Machine Learning" to find relevant openings.Application: Follow the application process outlined in the job posting. This often involves submitting your resume, cover letter, and sometimes additional materials like a portfolio or work samples.Tailor Your Application: Customize your resume and cover letter for each application, highlighting the skills and experiences that align with the specific role and Google's requirements.Showcase Projects: Highlight any Data Science projects you completed during your course, emphasizing the skills and technologies you used. If you have real-world experience or personal projects, include those as well.Google Cloud and Tools: Familiarize yourself with Google Cloud Platform (GCP) and tools like Google Colab, BigQuery, and TensorFlow. Proficiency in these tools can demonstrate your practical knowledge.Networking: Connect with current and former Google employees on LinkedIn. Attend networking events, conferences, or workshops related to Data Science to expand your professional network.Online Courses and Certifications: Google offers various online courses and certifications (Google Cloud Certifications, TensorFlow Certifications) that can boost your resume and demonstrate your commitment to learning relevant skills.Prepare for Interviews: If you get an interview call, prepare thoroughly. Research common interview questions for Data Science roles and practice your responses. Be ready to discuss your course, projects, and experiences.Behavioral Interviews: Google often conducts behavioral interviews to assess your fit with their culture. Prepare examples of how you've demonstrated key qualities like collaboration, problem-solving, and leadership.Coding and Technical Interviews: Depending on the role, you might have coding or technical interviews. Practice coding exercises and review Data Science concepts related to the role.Problem-Solving: Google values creative problem-solving. Practice solving hypothetical or real-world Data Science problems to demonstrate your analytical skills.Stay Persistent: The hiring process at Google can be competitive and may involve multiple rounds of interviews. Stay persistent, keep applying to suitable roles, and continue improving your skills.Remember that landing a job at Google can be competitive, so keep honing your skills, learning, and gaining relevant experience to increase your chances of success. Good luck on your journey to joining Google!Structured Learning Assistance - SLA is known for its excellence in education sector who provides Data Science Course in Delhi with a well established infrastructure with advanced lab facility, senior industry expert and modern training system to enhance the knowledge of students. SLA is well known entity having Data Science Institute. It is built with all convenient facilities and positive study ambiance for the students. Fulfill the main task of placement, SLA aids in 100% Job Placement after completion of 70% course.

Data Science Courses After 12th: EligibilityThe general eligibility criteria to pursue any data Science course after 12th preferably a diploma/bachelor’s/master’s is graduation in Mathematics, Computer Science, Engineering, Statistics, Data Science, or related fields. Aspirants need to score above 50% in graduation. Top data Science colleges such as IIT Madras ask for a minimum of 60% marks in graduation to be eligible for data Science courses. CourseEligibility CriteriaData Science CertificationsHigh school from a recognised board, and basic knowledge of maths, computer science, and statistics. Some colleges/institutes may ask for a 50% aggregate score in high schoolDiploma in Data ScienceBE/BTech/MCA/MSc degree with a minimum aggregate of 50% and statistics, computer, or programming as core subjects.BSc/BTech/BCA Data ScienceClass 10+2 from a recognised board with PCM and Computer Science/StatisticsMSc/MTech/MCA Data ScienceBCA/BSc Statistics/BSc Mathematics/BSc Computer Science/BSc IT/BE or BTech or equivalent degree from a recognised university with 50% marks. Candidates with knowledge of mathematics and statistics are preferredPhD in Data Science55% marks in master’s degree in any discipline. Candidates with a higher GPA will be preferred

 The programme typically consists of courses, research projects, and internships to prepare students to work with large, complex datasets and develop advanced techniques to extract valuable insights. Most Data Science Master's Degree programs require students to have a background in computer science, mathematics, statistics, or a related field. Some programs also require students to have practical experience in data Science or statistics before starting the program. Students can attend Data Science Master's Degree programs on-campus or online, depending on their preference and availability. The length of a Data Science Master's Degree programme can vary from one to two years, depending on the school and the program's format. Some programs also offer the option of pursuing the degree part-time, allowing students to work while earning their degree. Graduates of Data Science Master's Degree programs can work in various roles, such as data analysts, data scientists, machine learning engineers, and business analysts. Data Science is a rapidly growing field, and graduates of Data Science Master's Degree programs are in high demand across various industries, including healthcare, finance, technology, and more. Overall, a Data Science Master's Degree is an excellent path for individuals who are interested in working with data, developing data-driven solutions, and being a part of one of the fastest-growing fields in the world. Pursuing a Data Science Master's Degree can provide individuals with a solid foundation for a rewarding career and offer promising future prospects in the dynamic field of data science