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 utilizing scientific methods, algorithms, and systems to extract insights from structured and unstructured data.
Data Science involves collecting, cleaning, and analyzing data to uncover patterns and insights, aiding decision-making and addressing complex problems.
Data Science is applied across domains like finance, healthcare, marketing, and technology, tackling challenges such as fraud detection, personalized medicine, and customer analytics.
Key components of a Data Science pipeline include data collection, cleaning, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
Machine learning, a subset of Data Science, employs languages like Python, contributing to tasks such as classification, regression, and clustering.
Machine learning within Data Science involves building models that learn from data to make predictions or decisions, contributing to various tasks and applications.
Big Data involves handling massive datasets, and Data Science often utilizes Big Data technologies to extract insights from large-scale data.
Industries like finance use Data Science for risk analysis, healthcare for predictive modeling, and retail for demand forecasting, showcasing its versatile applications.
Data Science encompasses broader tasks, including data analysis, while machine learning specifically focuses on building models that learn from data.
Individuals with a background in mathematics, statistics, computer science, or related fields, coupled with a curiosity for data analysis, can pursue Data Science certification courses.
While Python proficiency is commonly required for data science, some roles may accept other languages, given the valuable skills and extensive support Python offers.
Build a compelling data science portfolio by showcasing projects with clear problem statements, thorough data exploration, analysis, and visualization, along with detailed explanations of your approach and findings.
Transitioning from a non-coding background to data science is achievable through dedication, self-learning, and relevant courses. Begin with basic coding skills and progress to advanced topics.
Diverse educational backgrounds are accepted, with degrees in computer science, statistics, mathematics, or related fields being common. However, practical skills and experience often carry significant weight.
Key skills for a Data Scientist include programming (e.g., Python), statistical knowledge, machine learning expertise, data wrangling, and effective communication.
Develop a robust data science portfolio by working on real-world projects, participating in online competitions, and continuously enhancing your skills.
Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications.
Emerging trends in data science include automated machine learning, explainable AI, and an increased focus on ethical considerations in data usage.
The career path for a Data Scientist in Colombia typically involves starting as a Junior Data Scientist, progressing to a Data Scientist, and potentially advancing to roles like Lead Data Scientist or Data Science Manager.
Initiate a career in data science in Colombia by acquiring relevant skills, networking with professionals, participating in local events, and applying for internships or entry-level positions in companies with a data science focus.
The salary of a data scientist in Colombia ranges from COP 88,036,036 per year according to an Economic Research Institute report.
The Datamites™ Certified Data Scientist course is intricately crafted to cover crucial aspects of data science, encompassing programming, statistics, machine learning, and business knowledge. With a focus on Python as the primary programming language, the course also accommodates professionals familiar with R. Offering a comprehensive foundation and addressing contemporary data science topics, the course prepares individuals with extensive knowledge. Successful completion, coupled with the IABAC™ certificate, positions participants as proficient data science professionals, well-equipped for the challenges in the field.
While a statistical background is advantageous, it is not always mandatory for pursuing a data science career in Colombia. Proficiency in pertinent tools, programming languages, and effective problem-solving skills often takes precedence in the hiring process.
For individuals in Colombia entering the data science realm, there are various introductory training options available, including courses like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.
Absolutely, DataMites in Colombia offers a diverse array of courses tailored for professionals seeking to elevate their expertise. 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 in Colombia offered by DataMites spans 8 months.
Career mentoring sessions at DataMites adopt an interactive format, delivering personalized guidance on resume building, interview preparation, and career strategies. These sessions offer valuable insights and tactics to enhance participants' professional journeys in the field of data science.
Upon successful completion of DataMites' Data Science Training in Colombia, participants are awarded the prestigious IABAC Certification. This globally recognized certification serves as a validation of their proficiency in data science concepts and applications, enhancing their credibility in the field.
To excel in data science, it is essential to establish a strong foundation in mathematics, statistics, and programming. Develop analytical skills, proficiency in languages like Python or R, and gain hands-on experience with tools like Hadoop or SQL databases.
Online data science training in Colombia from DataMites provides flexibility, allowing learners to progress at their own pace. It overcomes geographical barriers, making courses accessible to anyone with an internet connection. The training ensures a comprehensive syllabus, aligning with industry requirements, and features skilled instructors, fostering an interactive learning experience.
The data science training fee in Colombia varies from COP 2,076,859 to COP 5,192,738 depending on the specific program.
Certainly, DataMites integrates practical learning into the Data Scientist Course in Colombia, including over 10 capstone projects and a dedicated client/live project. This hands-on experience enhances participants' skills, providing real-world applications and industry-relevant exposure.
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.
Indeed, participants will receive a Certificate of Completion from DataMites upon finishing the Data Science Course, validating their proficiency in data science.
Participants are required to bring a valid Photo ID Proof, such as a National ID card or Driving License, to obtain a Participation Certificate and schedule the certification exam as needed.
In case of a missed session in the DataMites Certified Data Scientist Course in Colombia, participants usually have the option to access recorded sessions or attend support sessions to make up for missed content and clarify doubts.
Certainly, potential participants at DataMites can attend a demo class before making any payment for the Certified Data Scientist Course in Colombia to assess the teaching style, course content, and overall structure.
Yes, DataMites incorporates internships into its certified data scientist course in Colombia, providing a unique learning experience that combines theoretical knowledge with practical industry exposure, enhancing skills and job opportunities.
Upon successful completion of the Data Science training, you will be granted an internationally recognized IABAC® certification, confirming your proficiency in the field and elevating your employability on a global level.
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.