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 integrates scientific methodologies, algorithms, and systems across disciplines to derive insights from both structured and unstructured data.
Data Science encompasses the processes of collecting, cleaning, and analyzing data to reveal patterns and insights, ultimately supporting decision-making and addressing intricate problems.
Data Science finds application in various domains such as finance, healthcare, marketing, and technology, addressing challenges like fraud detection, personalized medicine, and customer analytics.
Crucial elements of the Data Science pipeline include data collection, cleaning, exploratory data analysis, feature engineering, model training, evaluation, and deployment.
Python is a prevalent programming language in Data Science, particularly within machine learning, where it plays a key role in tasks like classification, regression, and clustering.
Machine Learning, as a subset of Data Science, involves constructing models that learn from data, contributing significantly to tasks and applications within the broader field.
Big Data, centered around handling massive datasets, is often leveraged by Data Science to extract valuable insights from extensive and complex data.
Industries like finance utilize Data Science for risk analysis, healthcare for predictive modeling, and retail for demand forecasting, showcasing the versatile applications of this field.
While Data Science covers a broader spectrum of tasks, including data analysis, machine learning specifically concentrates on developing models that learn from data to make predictions or decisions.
Individuals possessing a background in mathematics, statistics, computer science, or related fields, coupled with a curiosity for data analysis, are eligible to pursue certification courses in Data Science.
While data science roles often require Python proficiency, some positions may consider expertise in alternative languages, recognizing the valuable skills and extensive support provided by Python.
Crafting a compelling data science portfolio involves presenting projects with well-defined problem statements, thorough exploration, analysis, and visualization of data, complemented by detailed explanations of methodologies and discoveries.
Transitioning from a non-coding background to data science is feasible through commitment, self-learning, and relevant courses. A recommended approach involves starting with foundational coding skills and progressively delving into advanced topics.
While diverse educational backgrounds are acceptable, degrees in computer science, statistics, mathematics, or related fields are commonly sought for a career in Data Science. Practical skills and hands-on experience carry significant weight.
Critical skills for a Data Scientist encompass proficiency in programming languages (such as Python), statistical knowledge, expertise in machine learning, adept data wrangling abilities, and effective communication.
Building a robust data science portfolio involves actively engaging in real-world projects, participating in online competitions, and consistently honing and updating skills to showcase one's expertise effectively.
Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications, underscoring the diverse and widespread demand for data science expertise.
Emerging trends in Data Science encompass the ascendancy of automated machine learning, a heightened emphasis on explainable AI, and an increased awareness of ethical considerations in the use of data.
The typical career path for a Data Scientist in Ivory Coast involves commencing as a Junior Data Scientist, advancing to a Data Scientist role, and potentially ascending to leadership positions such as Lead Data Scientist or Data Science Manager.
Commencing a career in data science in Ivory Coast involves acquiring relevant skills, networking with professionals, actively participating in local events, and seeking internships or entry-level positions in companies with a focus on data science.
The Datamites™ Certified Data Scientist course is thoughtfully designed to encompass essential facets of data science, including programming, statistics, machine learning, and business acumen. Emphasizing Python as the core language, the course caters to professionals familiar with R, providing a robust foundation and addressing contemporary data science topics. Completion of the course, crowned with the IABAC™ certificate, positions individuals as adept data science professionals ready to tackle industry challenges.
While beneficial, a statistical background is not always a prerequisite for embarking on a data science career in Ivory Coast. Proficiency in relevant tools, programming languages, and effective problem-solving skills often holds greater significance in the hiring process.
For individuals new to data science in the Ivory Coast, there are several introductory training options, including Certified Data Scientist, Data Science Foundation, and Diploma in Data Science courses.
Certainly, DataMites in Ivory Coast offers specialized courses tailored for professionals seeking to enhance their expertise. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and certifications in Operations, Marketing, HR, and Finance.
The duration of DataMites' data scientist course in Ivory Coast spans 8 months.
Career mentoring sessions at DataMites follow an interactive format, offering personalized guidance on resume building, interview preparation, and career strategies. These sessions provide valuable insights and tactics to enhance participants' professional journeys in the field of data science.
Upon successful completion of DataMites' Data Science Training in Ivory Coast, 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 Ivory Coast 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 Ivory Coast at DataMites varies from CFA 319,569 to CFA 799,013 depending on the specific program.
Certainly, DataMites integrates practical learning into the Data Scientist Course in Ivory Coast, 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 enroll in 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 Ivory Coast, 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 Ivory Coast to assess the teaching style, course content, and overall structure.
Yes, DataMites incorporates internships into its certified data scientist course in Ivory Coast, 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.