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 operates at the intersection of various disciplines, utilizing scientific methodologies, algorithms, and systems to extract meaningful insights from both structured and unstructured data.
The operational process of Data Science entails the systematic collection, cleansing, and analysis of data to unveil patterns and insights, thereby assisting in decision-making and addressing intricate problems.
Data Science applications span diverse sectors like finance, healthcare, marketing, and technology, addressing challenges such as fraud detection, personalized medicine, and customer analytics.
Critical stages in a Data Science pipeline include data collection, cleansing, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
In the realm of machine learning, a subset of Data Science, languages like Python play a crucial role, contributing to tasks such as classification, regression, and clustering.
The integration of machine learning within Data Science involves constructing models capable of learning from data and facilitating predictions or decisions across various tasks and applications.
Big Data, characterized by extensive datasets, aligns closely with Data Science, utilizing advanced technologies to extract insights and patterns from vast amounts of data.
Industries such as finance leverage Data Science for risk analysis, healthcare employs it for predictive modeling, and retail utilizes it for demand forecasting, showcasing the widespread applications of this field.
While Data Science encompasses a broader range of tasks, including data analysis, machine learning specifically focuses on constructing models that learn from data to make predictions.
Individuals with backgrounds in mathematics, statistics, computer science, or related fields, coupled with a keen interest in data analysis, are eligible to pursue Data Science certification courses.
While Python proficiency is commonly required in data science, some roles may accept proficiency in other languages, recognizing the valuable skills and extensive support Python offers.
Crafting a compelling data science portfolio involves showcasing projects with clear problem statements, thorough data exploration, analysis, and visualization, accompanied by detailed explanations of approaches and findings.
Transitioning from a non-coding background to data science is achievable through dedication, self-learning, and relevant courses. Starting with basic coding skills and progressing to advanced topics is a recommended approach.
While diverse educational backgrounds are accepted, degrees in computer science, statistics, mathematics, or related fields are common for a Data Science career. Practical skills and experience are often valued.
Essential skills for a Data Scientist encompass programming (e.g., Python), statistical knowledge, expertise in machine learning, data wrangling capabilities, and effective communication.
Building a robust data science portfolio involves actively working on real-world projects, participating in online competitions, and consistently improving and updating one's skills.
Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications, indicating a broad spectrum of demand.
Emerging trends in Data Science include the rise of automated machine learning, increased focus on explainable AI, and a heightened awareness of ethical considerations in data usage.
The career trajectory for a Data Scientist in Bogotá typically involves starting as a Junior Data Scientist, advancing to a Data Scientist role, and potentially moving into leadership positions like Lead Data Scientist or Data Science Manager.
Embarking on a data science career in Bogotá involves acquiring relevant skills, networking with professionals, participating in local events, and seeking internships or entry-level positions in companies with a focus on data science.
The salary of a data scientist in Bogota ranges from COP 1,32,00,000 per year according to a Glassdoor report.
The Datamites™ Certified Data Scientist course is meticulously designed to cover essential facets of data science, including programming, statistics, machine learning, and business knowledge. Emphasizing Python as the core language, the course accommodates professionals familiar with R, offering a comprehensive foundation and addressing contemporary data science topics. Completion, crowned with the IABAC™ certificate, positions individuals as proficient data science professionals ready for field challenges.
While advantageous, a statistical background is not always mandatory for a data science career in Bogotá. Proficiency in tools, programming languages, and problem-solving skills often takes precedence in hiring.
DataMites in Bogotá offers a range of programs, including Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, and specialized certifications in areas like Operations, Marketing, HR, and Finance.
Newcomers in Bogotá can explore introductory training options like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science courses.
Certainly, DataMites in Bogotá offers specialized courses for professionals, including Statistics for Data Science, Data Science with R Programming, Python for Data Science, and certifications in Operations, Marketing, HR, and Finance.
The data science course in Bogotá offered by DataMites spans a duration of 8 months.
Career mentoring sessions at DataMites are interactive, providing personalized guidance on resume building, interview preparation, and career strategies to enhance participants' professional journeys in data science.
Upon completing DataMites' Data Science Training in Bogotá, participants receive the globally recognized IABAC Certification, validating their proficiency in data science concepts and applications.
Exceling in data science requires a strong foundation in mathematics, statistics, programming, analytical skills, proficiency in Python or R, and hands-on experience with tools like Hadoop or SQL databases.
Online data science training in Bogotá from DataMites offers flexibility, overcoming geographical barriers, ensuring a comprehensive syllabus, industry-relevant content, skilled instructors, and engaging learning experiences.
The data science training fee in Bogotá varies from COP 2,076,859 to COP 5,192,738, depending on the specific program.
Certainly, DataMites integrates practical learning with over 10 capstone projects and a dedicated client/live project, providing hands-on experience and industry-relevant exposure.
Instructors at DataMites are selected based on certifications, extensive industry experience, and expertise in the subject matter.
DataMites offers flexible learning methods, including Live Online sessions and self-study, tailored to participants' preferences.
The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, reviewing topics, addressing doubts, and solidifying comprehension for a comprehensive understanding.
Upon completion, participants receive a Certificate of Completion from DataMites, validating their proficiency in data science.
Participants need to bring a valid Photo ID Proof, like a National ID card or Driving License, to obtain a Participation Certificate and schedule the certification exam.
In case of a missed session, participants usually have the option to access recorded sessions or attend support sessions to catch up and clarify doubts.
Certainly, potential participants can attend a demo class before making any payment for the Certified Data Scientist Course in Bogotá to assess the teaching style and course content.
Yes, DataMites includes internships in its certified data scientist course in Bogotá, providing practical industry exposure to enhance skills and job opportunities.
Upon successful completion, participants receive an internationally recognized IABAC® certification, confirming their proficiency in data science.
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.