Instructor Led Live Online
Self Learning + Live Mentoring
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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 a multidisciplinary field that employs scientific methodologies, algorithms, and systems to extract valuable insights from both structured and unstructured data.
The process of Data Science involves the collection, cleaning, and analysis of data to reveal patterns and insights, thereby facilitating informed decision-making and addressing intricate problems.
Data Science finds applications in diverse domains such as finance, healthcare, marketing, and technology, where it plays a crucial role in tasks like fraud detection, personalized medicine, and customer analytics.
Essential elements of a Data Science pipeline encompass data collection, cleaning, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
In the realm of machine learning, a subset of Data Science, languages like Python are prevalent, contributing to tasks such as classification, regression, and clustering.
Machine Learning is integral to Data Science, involving the development of models that learn from data to make predictions or decisions, contributing to a myriad of tasks and applications.
Big Data involves managing extensive datasets, and Data Science often leverages Big Data technologies to derive insights from large-scale data sets.
Industries like finance utilize Data Science for risk analysis, healthcare for predictive modeling, and retail for demand forecasting, showcasing the versatility of Data Science applications.
While Data Science encompasses a broader range of tasks, including data analysis, machine learning specifically focuses on constructing models that learn from data.
Individuals with a background in mathematics, statistics, computer science, or related fields, coupled with a keen interest in data analysis, can pursue certification courses in Data Science.
Before embarking on a data science journey with Python, it's crucial to possess a foundational proficiency in the language. However, some data science roles might consider alternative languages, emphasizing the importance of valuable skills and Python's extensive support.
Crafting a compelling data science portfolio involves showcasing projects with well-defined problem statements, comprehensive data exploration, analysis, and visualization. It's essential to provide detailed explanations of your approach and findings to demonstrate your expertise effectively.
Shifting from a non-coding background to data science is achievable through dedicated self-learning and relevant courses. Starting with basic coding skills and progressively advancing to more complex topics is a recommended approach.
While diverse educational backgrounds are acceptable, common degrees include computer science, statistics, mathematics, or related fields. Practical skills and hands-on experience often carry significant weight in the field.
Critical skills for a Data Scientist encompass proficiency in programming languages like Python, statistical knowledge, expertise in machine learning, and effective communication. Data wrangling skills are also crucial for success in this field.
Building a robust data science portfolio involves working on real-world projects, engaging in online competitions, and continually enhancing your skills to showcase your expertise and problem-solving capabilities.
Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications, highlighting the widespread applicability of data science across various sectors.
Emerging trends in data science include the rise of automated machine learning, increased focus on explainable AI, and a growing emphasis on ethical considerations in data usage.
The typical career path for a Data Scientist in Afghanistan involves starting as a Junior Data Scientist, advancing to a Data Scientist role, and potentially reaching higher positions such as Lead Data Scientist or Data Science Manager.
Initiating a career in data science in Afghanistan involves acquiring relevant skills, networking with professionals in the field, participating in local events, and actively seeking internships or entry-level positions in companies with a focus on data science.
The Datamites™ Certified Data Scientist course is meticulously designed to delve into key facets of data science, including programming, statistics, machine learning, and business knowledge. Centered around Python as the primary programming language, the course is inclusive for professionals familiar with R. By providing a robust foundation and addressing contemporary data science themes, the course equips individuals with comprehensive knowledge. Upon successful completion and the attainment of the IABAC™ certificate, participants emerge as adept data science professionals well-prepared for the industry's challenges.
While a background in statistics can be beneficial, it is not always obligatory to pursue a career in data science in Afghanistan. The emphasis often lies on proficiency in relevant tools, programming languages, and effective problem-solving skills during the hiring process.
For those venturing into the realm of data science in Afghanistan, numerous introductory training options await, such as Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.
Certainly, DataMites in Afghanistan offers a diverse range of courses designed for professionals looking to augment their expertise. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and specialized certifications in Operations, Marketing, HR, and Finance.
The data science course offered by DataMites in Afghanistan spans 8 months.
Career mentoring sessions at DataMites follow an interactive format, providing personalized guidance on resume building, interview preparation, and career strategies. These sessions equip participants with valuable insights to enhance their professional journey in the field of data science.
Upon successful completion, participants receive the globally recognized IABAC Certification, validating their proficiency in data science concepts and applications.
To excel in data science, a strong foundation in mathematics, statistics, and programming is essential. Analytical skills, proficiency in Python or R, and hands-on experience with tools like Hadoop or SQL databases are also recommended.
Online data science training from DataMites in Afghanistan provides flexibility, overcoming geographical barriers and allowing learners to progress at their own pace. The comprehensive syllabus aligns with industry requirements, and skilled instructors facilitate an interactive learning experience.
The data science training fee in Afghanistan varies from AF 39,420 to AF 98,561 depending on the specific program.
Certainly, DataMites integrates practical learning into the Data Scientist Course in Afghanistan, offering over 10 capstone projects and a dedicated client/live project to enhance participants' skills with real-world applications.
Instructors selected for data science training at DataMites hold certifications, possess extensive industry experience, and demonstrate expertise in the subject matter.
DataMites offers flexible learning methods, including Live Online sessions and self-study, tailored to accommodate participants' preferences.
The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, enabling them to review topics, address doubts, and solidify comprehension across various sessions for a comprehensive understanding of the course content.
Certainly, participants will be presented with a Certificate of Completion from DataMites, validating their mastery of data science concepts and skills.
Participants are required to bring a valid Photo ID Proof, such as a National ID card or Driving License, to receive a Participation Certificate and schedule any necessary certification exams.
In the event of a missed session in the DataMites Certified Data Scientist Course in Afghanistan, participants typically have the opportunity to access recorded sessions or attend support sessions to catch up on missed content and address any queries.
Certainly, prospective participants at DataMites can attend a demo class before making any payment for the Certified Data Scientist Course in Afghanistan. This allows them to assess the teaching style, course content, and overall structure before committing.
Yes, DataMites integrates internships into its certified data scientist course in Afghanistan, offering a comprehensive learning experience that combines theoretical knowledge with practical industry exposure. This approach enhances skills and opens up new job opportunities.
Upon successful completion of the Data Science training, you will be awarded an internationally recognized IABAC® certification. This certification serves as a testament to your proficiency in the field, boosting your employability on a global scale.
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.