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 an interdisciplinary field that utilizes scientific methodologies, algorithms, and systems to extract meaningful insights from structured and unstructured data.
The functioning of Data Science involves the systematic collection, cleaning, and analysis of data to uncover patterns and insights, facilitating informed decision-making and addressing complex problems.
Data Science is applied across various domains such as finance, healthcare, marketing, and technology, playing a vital role in tasks like fraud detection, personalized medicine, and customer analytics.
Critical elements of a Data Science pipeline include data collection, cleaning, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
In the machine learning subset of Data Science, prevalent languages like Python are extensively used for tasks such as classification, regression, and clustering.
Machine Learning is integral to Data Science, involving the creation of models that learn from data to make predictions or decisions, contributing to a wide array of tasks and applications.
Big Data, which deals with extensive datasets, is often harnessed by Data Science using technologies designed for large-scale data analysis and interpretation.
Industries like finance employ Data Science for risk analysis, healthcare for predictive modeling, and retail for demand forecasting, highlighting the versatile applications of Data Science.
While Data Science encompasses a broader spectrum of tasks, including data analysis, machine learning specifically focuses on constructing models that learn from data for predictive purposes.
Individuals with a foundation in mathematics, statistics, computer science, or related fields, coupled with a keen interest in data analysis, are well-suited for pursuing certification courses in Data Science.
Before venturing into data science with Python, it is essential to have a solid foundation in the language. However, certain data science roles may consider other languages, emphasizing the importance of valuable skills and Python's extensive support.
Developing a compelling data science portfolio involves presenting projects with well-defined problem statements, thorough data exploration, analysis, and visualization. Providing detailed explanations of your approach and findings is crucial to effectively showcase your expertise.
Moving from a non-coding background to a career in data science is attainable 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 include 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 dynamic field.
Constructing a robust data science portfolio involves working on real-world projects, participating 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, underscoring the broad applicability of data science across diverse sectors.
Emerging trends in data science encompass the rise of automated machine learning, a heightened focus on explainable AI, and an increasing emphasis on ethical considerations in data usage.
The typical career trajectory for a Data Scientist in Kabul involves starting as a Junior Data Scientist, progressing to a Data Scientist role, and potentially attaining higher positions such as Lead Data Scientist or Data Science Manager.
Commencing a career in data science in Kabul involves acquiring relevant skills, networking with professionals in the field, engaging in local events, and actively pursuing internships or entry-level positions in companies with a focus on data science.
The Datamites™ Certified Data Scientist course thoroughly explores essential aspects of data science, covering programming, statistics, machine learning, and business knowledge. With a focus on Python as the primary programming language, the course is inclusive for those with familiarity with R. By delivering a strong foundation and addressing current data science trends, participants gain comprehensive knowledge. Successful completion and the acquisition of the IABAC™ certificate position individuals as skilled data science professionals ready for industry challenges.
While having a background in statistics can be advantageous, it is not always a compulsory prerequisite for pursuing a career in data science in Kabul. The emphasis often lies on proficiency in relevant tools, programming languages, and effective problem-solving skills during the hiring process.
Kabul offers various introductory training programs for those stepping into the domain of data science, including options like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.
DataMites in Kabul presents a diverse array of courses tailored for professionals seeking to boost their knowledge. These encompass 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 provided by DataMites in Kabul spans 8 months.
Career mentoring sessions at DataMites follow an interactive format, delivering personalized guidance on resume building, interview preparation, and strategic career advice. These sessions equip participants with valuable insights to enhance their professional journey in the dynamic field of data science.
Upon successful completion, participants are awarded the globally recognized IABAC Certification, validating their expertise in various data science concepts and applications.
To excel in data science, a solid foundation in mathematics, statistics, and programming is crucial. Analytical skills, proficiency in Python or R, and hands-on experience with tools like Hadoop or SQL databases are also recommended for a comprehensive learning experience.
Online data science training in Kabul from DataMites offers flexibility, overcoming geographical constraints and allowing learners to progress at their own pace. The industry-aligned syllabus and experienced instructors contribute to an interactive learning experience, meeting the demands of the evolving field.
The cost of data science training in Kabul with DataMites ranges from AF 39,420 to AF 98,561, depending on the specific program selected.
DataMites enhances the Data Scientist Course in Kabul with practical learning, featuring over 10 capstone projects and a dedicated client/live project to strengthen participants' skills through real-world applications.
Instructors leading data science training at DataMites are selected based on certifications, extensive industry experience, and demonstrated expertise in the subject matter.
DataMites provides flexible learning methods, including Live Online sessions and self-study options, tailored to accommodate participants' preferences.
The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, facilitating review sessions, doubt clarification, and a comprehensive understanding of course content across various sessions.
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 case of a missed session in the DataMites Certified Data Scientist Course in Kabul, participants typically have access to recorded sessions or support sessions to catch up on content and address queries.
Certainly, potential participants at DataMites can attend a demo class before making any payment for the Certified Data Scientist Course in Kabul, allowing them to evaluate the teaching style, course content, and overall structure.
Yes, DataMites integrates internships into its certified data scientist course in Kabul, providing a holistic learning experience that combines theoretical knowledge with practical industry exposure, enhancing skills and creating job opportunities.
Upon successful completion of the Data Science training, participants are awarded an internationally recognized IABAC® certification, serving as a testament to their proficiency in the field and enhancing employability globally.
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.