Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
The entire training includes real-world projects and highly valuable case studies.
IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.
MODULE 1: DATA SCIENCE COURSE INTRODUCTION
MODULE 2: DATA SCIENCE ESSENTIALS
MODULE 3: DATA SCIENCE DEMO
MODULE 4: ANALYTICS CLASSIFICATION
MODULE 5: DATA SCIENCE AND RELATED FIELDS
MODULE 6: DATA SCIENCE ROLES & WORKFLOW
MODULE 7: MACHINE LEARNING INTRODUCTION
MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS
MODULE 1: PYTHON BASICS
MODULE 2: PYTHON CONTROL STATEMENTS
MODULE 3: PYTHON DATA STRUCTURES
MODULE 4: PYTHON FUNCTIONS
MODULE 5: PYTHON NUMPY PACKAGE
MODULE 6: PYTHON PANDASPACKAGE
MODULE 1: OVERVIEW OF STATISTICS
MODULE 2: HARNESSING DATA
MODULE 3: EXPLORATORY DATA ANALYSIS
MODULE 4: HYPOTHESIS TESTING
MODULE 5: CORRELATION AND REGRESSION
MODULE 1: MACHINE LEARNING INTRODUCTION
MODULE 2: PYTHON NUMPY & PANDAS PACKAGE
MODULE 3: VISUALIZATION WITH PYTHON
MODULE 4: ML ALGO: LINEAR REGRESSION
MODULE 5: ML ALGO: KNN
MODULE 6: ML ALGO: LOGISTIC REGRESSION
MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA)
MODULE 8: ML ALGO: K MEANS CLUSTERING
MODULE 1: MACHINE LEARNING INTRODUCTION
MODULE 2: ML ALGO: LINEAR REGRESSSION
MODULE 3: ML ALGO: LOGISTIC REGRESSION
MODULE 4: ML ALGO: KNN
MODULE 5: ML ALGO: K MEANS CLUSTERING
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
MODULE 7: ML ALGO: DECISION TREE
MODULE 8 : ML ALGO: NAÏVE BAYES
MODULE 9: GRADIENT BOOSTING, XGBOOST
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
MODULE 12: ADVANCED ML CONCEPTS
MODULE 1: TIME SERIES FORECASTING - ARIMA
MODULE 2: FEATURE ENGINEERING
MODULE 3: SENTIMENT ANALYSIS
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
MODULE 8: AZURE FOR DATA SCIENCE
MODULE 1: DATABASE INTRODUCTION
MODULE 2: SQL BASICS
MODULE 3: DATA TYPES AND CONSTRAINTS
MODULE 4: DATABASES AND TABLES (MySQL)
MODULE 5: SQL JOINS
MODULE 6: SQL COMMANDS AND CLAUSES
MODULE 7 : DOCUMENT DB/NO-SQL DB
MODULE 1: GIT INTRODUCTION
MODULE 2: GIT REPOSITORY and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
MODULE 4: TAGGING, BRANCHING AND MERGING
MODULE 5: UNDOING CHANGES
MODULE 6: GIT WITH GITHUB AND BITBUCKET
MODULE 1: BIG DATA INTRODUCTION
MODULE 2 : HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 5 : MACHINE LEARNING WITH SPARK ML
MODULE 6: KAFKA and Spark
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
MODULE 2: BI WITH TABLEAU: INTRODUCTION
MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE
MODULE 4: TABLEAU : BUSINESS INSIGHTS
MODULE 5: DASHBOARDS, STORIES AND PAGES
MODULE 6: BI WITH POWER-BI
Data science is the field that involves extracting insights and knowledge from data using various techniques such as statistical analysis, machine learning, and data visualization.
Learning data science is important as it enables individuals to make data-driven decisions, solve complex problems, and uncover valuable insights from large datasets, leading to improved business strategies and innovation.
Key skills required to become a data scientist include proficiency in programming languages like Python or R, knowledge of statistics and mathematics, data manipulation and analysis, machine learning, data visualization, and problem-solving abilities.
To effectively acquire knowledge in data science, individuals can pursue online courses, attend workshops and boot camps, engage in hands-on projects, participate in online communities and forums, and stay updated with the latest industry trends and research papers.
Typical challenges encountered by data scientists include data cleaning and preprocessing, handling large and complex datasets, selecting appropriate models and algorithms, dealing with missing or incomplete data, and interpreting and communicating results effectively.
The cost of a data science course in Gangtok can vary depending on the institution and program, but it generally ranges from INR 40,000 to INR 50,000.
Prerequisites for enrolling in a data science course may include a background in mathematics and statistics, basic programming skills, knowledge of data analysis tools, and a strong desire to learn and explore the field of data science.
There are diverse career opportunities in data science, including data analyst, data scientist, machine learning engineer, business analyst, data engineer, and data consultant, across industries such as finance, healthcare, e-commerce, marketing, and technology.
Obtaining certification in data science is significant as it provides validation of skills and knowledge, enhances job prospects and career advancement opportunities, and demonstrates a commitment to professional growth in the field.
Yes, there is a high demand for data science courses in the industry due to the increasing reliance on data-driven decision-making, the need for skilled professionals to extract insights from large datasets, and the growing adoption of artificial intelligence and machine learning technologies.
Python is recommended as one of the top programming languages for data science due to its versatility, rich ecosystem of libraries and frameworks (e.g., NumPy, Pandas, TensorFlow), and ease of use for data manipulation, analysis, and machine learning tasks.
While a background in statistics is beneficial for data science, it is not necessarily a strict requirement. Understanding statistical concepts and methods helps in interpreting data and building accurate models, but it can be learned along with other data science skills.
Python alone can suffice for data science as it provides a wide range of libraries and tools specifically designed for data analysis, machine learning, and visualization. However, knowledge of additional languages like R can also be advantageous in certain contexts.
SQL (Structured Query Language) is highly useful for data scientists as it allows efficient querying and manipulation of relational databases, which are commonly used to store structured data. Proficiency in SQL is beneficial for data retrieval and integration tasks.
While coding is an essential part of data science, the extent of coding involved can vary depending on the specific tasks and projects. Data scientists often need to write code for data preprocessing, modeling, and analysis, but the level of complexity may vary based on the project requirements.
DataMites in Gangtok stands out as an outstanding choice for individuals looking to pursue a Data Science course. It boasts several key advantages, including highly skilled instructors, a comprehensive curriculum that encompasses various data science subjects, an emphasis on practical learning with hands-on exercises, industry-relevant projects, and dedicated support in finding placement opportunities.
DataMites in Gangtok warmly welcomes individuals with a strong foundation in mathematics and programming, as well as those with prior experience in statistics, engineering, or related fields, to enrol in their Certified Data Scientist Course. This inclusive approach ensures that individuals from diverse backgrounds can pursue their career aspirations in the dynamic and ever-evolving field of Data Science.
Opting for the DataMites data science course in Gangtok is a wise choice due to its carefully designed curriculum, knowledgeable faculty, engaging hands-on learning opportunities, practical project assignments, and industry-focused training. This extensive program significantly improves your knowledge and skills in the field of data science, thereby enhancing your chances of securing rewarding employment opportunities.
The course has a duration of 8 months, spanning 700 learning hours, with a dedicated allocation of 120 hours for live online training.
Upon the successful completion of the data science course in Gangtok, students receive the highly prestigious IABAC certification, which holds considerable international recognition. This esteemed certification serves as a valuable credential, expanding employment prospects and facilitating participation in internship programs, thus opening a wide range of opportunities in the field of data science.
DataMites provides strong support and guidance for placements through their dedicated Placement Assistance Team (PAT) after the completion of the course. The PAT offers individualized assistance to individuals, ensuring they receive comprehensive support in finding appropriate job placements. This tailored support greatly improves employment prospects and opens up a wide range of opportunities in the dynamic field of data science.
DataMites in Gangtok provides a diverse selection of data science courses that encompass a broad range of topics. These courses include Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Statistics for Data Science, Data Science Marketing, Data Science Operations, Data Science Retail, Data Science for HR, Data Science with Finance, and Data Science.
DataMites is widely recognized for its outstanding team of industry-expert educators who possess deep expertise and extensive experience in the field of data science. These highly qualified instructors hold prestigious certifications and bring their vast knowledge to the classroom, delivering exceptional instruction. Under their guidance, students are empowered to develop a comprehensive understanding of the subject matter.
DataMites recognizes the diverse preferences of students and provides flexible learning options to accommodate their needs. They offer a variety of choices, including live online sessions, self-paced learning, and on-demand classroom training. This flexibility allows individuals to select the learning approach that best suits their requirements, making it convenient for them to pursue their data science education.
DataMites provides a detailed overview of their training approach, ensuring that students have a clear understanding of the training process and its components. Moreover, they offer a complimentary demo class, allowing individuals to fully grasp the training methodology. This enables prospective students to evaluate the quality and suitability of the training before making a commitment, empowering them to make an informed decision.
Learning Through Case Study Approach
Theory → Hands-on → Case Study → Project → Model Deployment
The payment mode available for the data science course in Gangtok through:
DataMites offers its Data Science Course in Gangtok at different price points, providing a range of options to cater to diverse preferences. These options include INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training. This flexible pricing structure enables individuals to select the plan that fits their budget and preferred learning mode.
In order to receive the participation certificate and book the certification exam, it is essential to provide valid photo identification proofs, such as a National ID card or a Driver's license. These identification proofs play a crucial role in ensuring the authenticity and accuracy of the certification process.
The salary of data scientists in India ranges from INR 11,49,482 per year according to an Indeed report.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.