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: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions
MODULE 5: PYTHON NUMPY PACKAGE
• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
MODULE 6: PYTHON PANDAS PACKAGE
• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA SCIENCE
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: R LANGUAGE ESSENTIALS
• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures , control statements
MODULE 6: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 7: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification, And Regression
• Supervised Vs Unsupervised
MODULE 2: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 4: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 7: ML ALGO: DECISION TREE
• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 9: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
MODULE 12: ADVANCED ML CONCEPTS
• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross-validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set: Smote Technique
• Feature Selection Techniques
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC
MODULE 2: FEATURE ENGINEERING
• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling
MODULE 3: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model Deployment
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker
MODULE 8: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING, AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
MODULE 5: UNDOING CHANGES
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 6: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2: HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language
MODULE 5: MACHINE LEARNING WITH SPARK ML
• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest
MODULE 6: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
MODULE 2: BI WITH TABLEAU: INTRODUCTION
• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE
• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
MODULE 4: TABLEAU: BUSINESS INSIGHTS
• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering
MODULE 5: DASHBOARDS, STORIES AND PAGES
• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
MODULE 6: BI WITH POWER-BI
• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management
MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
MODULE 2: DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE 3: TENSORFLOW FOUNDATION
• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow
MODULE 4: COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm
MODULE 6: AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
DataMites™ Certified Data Scientist Training is designed to provide a right blend of all four facets of Data Science
This course comes as a perfect package of required Data Science skills including programing, statistics and Machine Learning. If you aspire to be Data Science professional, this course can immensely help you to reach your goal.
After successful completion of this “Certified Data Scientist” course, you should have
Data science is the hottest field in the market as of today. Be it a small company or an MNC, they need a Data scientist to manage their large pool of data.
This course “Certified Data scientist” is not restricted to any specific domain.
DataMites™ is the global institute for Data Science accredited by International Association of Business Analytics Certifications (IABAC). DataMites provides flexible learning options from Classroom training, Live Online to high quality recorded sessions
The 6 Key reasons to choose Data Mites™
IABAC™ Accredited
Elite Faculty & Mentors
Learning Approach
10+ Industry Projects
PAT (Placement Assistance Team)
24x7 Cloud Lab for ONE year
Data, in its vast and complex form, holds the potential to be transformed into valuable information. In the field of data science, the focus lies on extracting meaningful insights from large datasets, comprising both structured and unstructured data. By employing advanced techniques, data scientists are able to uncover hidden patterns and uncover actionable insights that can drive decision-making and create tangible value. Through the process of mining and analyzing data, data science brings forth the power to unlock the true potential of information.
Individuals of all backgrounds, whether newcomers or seasoned professionals, who possess an interest in learning Data Science, can readily pursue this field. Engineers, marketing professionals, software and IT professionals, among others, have the opportunity to enroll in part-time or external data science programs. Regular data science courses typically require a minimum prerequisite of basic high school level subjects.
The cost of data science courses may vary depending on the level of training you seek. When considering the fee structure for classroom training in data science, it typically ranges from 1000 USD to 3000 USD, depending on the training provider you choose.
The basic skills required to learn Data Science include programming knowledge (such as Python or R), statistical analysis, data manipulation and visualization, machine learning algorithms, and critical thinking/problem-solving abilities.
Proficiency in programming languages such as Python, R, Excel, C++, Java, and SQL is highly preferred in the field of Data Science. However, it is possible to start with the fundamentals and continually enhance your skills in these areas.
Data Science can be challenging due to its complex concepts and techniques. It requires a solid understanding of mathematics, statistics, programming, and domain knowledge. However, with dedication, proper learning resources, and practice, it is possible to overcome the challenges and become proficient in Data Science.
Python, R, SQL, Tableau, Apache Spark, TensorFlow, and PyTorch are commonly used tools in Data Science.
Data science has now surpassed almost every business on the earth. There isn't a single industry on the planet that doesn't rely on data these days. As a result, data science has turned into a source of energy for companies. Data Science is applicable in industries including travel, healthcare, sales, credit and insurance, marketing, social media, automation and substantially more!
By 2025, global data is projected to reach 175 zettabytes, as reported by IDC. Data Science plays a crucial role in helping companies effectively analyze and utilize large volumes of data from various sources, enabling them to make informed and data-driven decisions. Its applications span across diverse industries including marketing, healthcare, finance, banking, and policy work. The importance of data science is undeniable, given its widespread use and impact.
Yes, it is definitely possible to switch from a mechanical background to a career in data science. While a background in mechanical engineering may not directly align with data science, it can provide you with a strong foundation in mathematics, problem-solving skills, and analytical thinking, which are valuable in the field of data science.
Freshers are indeed hired for Data Scientist positions in companies. In India, many entry-level analytics jobs do not require any specialization or post-graduation. The primary qualification sought by these companies is an engineering degree, irrespective of the stream. They focus on assessing your aptitude, communication skills, and critical reasoning abilities rather than specific academic backgrounds.
Yes, it is possible to switch from a non-coding background to a data science background. While having prior coding experience can be advantageous, it is not a strict requirement for entering the field of data science. Many individuals with non-coding backgrounds have successfully transitioned into data science roles.
The field of Data Science is extensive, and its applications are limitless. Companies worldwide are actively seeking data science professionals who can contribute to their organizations. Obtaining data science certifications can be highly beneficial for your career in today's technology-driven world. It enhances your skillset and increases your prospects in the job market.
Total course fee should be paid before 50% of the course completion. We also have EMI option tied up with bank. Check with coordinators.
No, most of the software is free and open source. The guidelines to setup software are a part of course.
Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected cities in India such as Bangalore, Hyderabad.
Yes. The IABAC Exam fee is included in the course fee. No extra fee is charged.
All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.
We have a dedicated PAT (Placement Assistance team) to provide 100% support in finding your dream job.
Yes, statistics is an essential component of data science and plays a crucial role in achieving accurate results and making informed decisions. Statistics allows data scientists to analyze and interpret data, apply various statistical techniques like classification, regression, hypothesis testing, and time series analysis, and build robust data models. It provides the foundation for understanding data patterns, relationships, and uncertainties, ultimately improving the quality of insights and driving effective decision-making in data science.
Python is widely regarded as a fundamental tool in the field of data science. In fact, a significant majority of data scientists, around 66% according to a 2018 survey, reported using Python on a daily basis. Python's popularity stems from its versatility, extensive libraries and frameworks, and ease of use for data manipulation, analysis, and machine learning tasks. While there are other programming languages used in data science, having proficiency in Python or any programming language is considered essential to effectively carry out data science work.
DataMites renders Data Science Training in:
At Datamites, the following beginner-level data science courses are available:
Certified Data Scientist (CDS): This course is designed for individuals who want to start their journey in data science. It covers the fundamentals of data science, including Python programming, data analysis, machine learning algorithms, data visualization, and model deployment. The course also includes hands-on projects and case studies to provide practical experience.
Data Science Foundation: This course provides a solid foundation in data science concepts and techniques. It covers topics such as Python programming, data manipulation, exploratory data analysis, statistical analysis, machine learning algorithms, and data visualization. The course aims to equip students with the essential skills needed to kickstart a career in data science.
Diploma in Data Science: This comprehensive program is designed for beginners and covers a wide range of data science topics. It includes modules on Python programming, data preprocessing, exploratory data analysis, feature engineering, machine learning algorithms, deep learning, natural language processing, and big data analytics. The course incorporates hands-on projects and industry-relevant case studies to enhance practical skills.
offers a course specifically designed for C-level executives and business owners called "Data Science for Managers." This course focuses on providing a comprehensive understanding of data science concepts, strategies, and applications from a managerial perspective. The course aims to enhance the data science competency of managers and business leaders, enabling them to make informed decisions based on data-driven insights.
The Data Science Course has a duration of 8 months, with a total of 700 hours of training. Training sessions are available on both weekdays and weekends, allowing you to choose the option that best suits your availability and schedule.
While a postgraduate degree is not necessarily a requirement, having prior knowledge in areas such as Mathematics, Statistics, Economics, or Computer Science can greatly benefit your understanding and proficiency in Data Science. These foundational subjects provide a solid basis for grasping the concepts and techniques used in the field. However, even if you don't have a PG degree or specific background, with dedication and the right resources, you can still learn and excel in Data Science.
Data Science is one of the best spheres where you can begin your career in. Freshers can enroll for Certified Data Scientist Course and Data Science Foundation Course or Diploma in Data Science.
Professionals who wish to enhance their professional capabilities can enroll for:
DataMites offers specialized courses for senior managers and business owners, including courses on;
A certified data scientist is an individual who has obtained comprehensive knowledge in the field of data science. The Certified Data Scientist Training is specifically tailored for those who aspire to enter the Data Science domain with a strong foundation and the necessary skills to excel in this field. The course provides thorough guidance and equips participants with the best practices and expertise required to succeed in the data science industry. By completing the course and earning the certification, individuals can demonstrate their proficiency and readiness to tackle real-world data science challenges.
The CDS (Certified Data Scientist) Course is specifically designed for aspiring data science professionals who are new to the field and aim to make a significant impact in the world of Data Science. This course is structured to provide comprehensive knowledge and skills required to excel in the field of data science.
The fees for the Data Science Course will vary depending on the specific program and level of training you choose.
DataMites provides classroom training for Data Science courses primarily in Bangalore. However, we understand the demand for training in other locations as well. We are open to hosting classroom sessions in other locations based on the demand and availability of interested applicants in those areas. Please contact us with your location preference, and we will do our best to accommodate your needs and schedule a training session in your desired location.
At DataMites, we prioritize the selection of trainers who possess the required qualifications and extensive expertise in the field of Data Science. Our trainers are carefully selected based on their industry experience, certification, and comprehensive understanding of the subject matter. We strive to ensure that our trainers have accumulated decades of practical experience and are well-versed in the latest trends and techniques in the field of Data Science. Rest assured that our trainers are highly knowledgeable and capable of delivering high-quality training to our students.
At DataMites, we understand that everyone has different learning preferences. That's why we offer flexible options including live online, self-paced, and classroom training. Choose the method that suits you best and embark on your data science journey with us.
With the DataMites Flexi-Pass for Data Science training, you'll have a 3-month window to attend our sessions. Take advantage of this opportunity to clarify doubts and review topics according to your preference. We are dedicated to supporting your learning journey every step of the way.
Upon successful completion of the Data Science training, you will be awarded an internationally recognized IABAC® certification, validating your proficiency in the field and enhancing your global employability.
Upon successful completion of the course, you will receive a Course Completion Certificate from us, acknowledging your successful accomplishment and demonstrating your dedication and competence in the field of Data Science.
Yes, to ensure authenticity and accuracy, we require participants to provide a valid photo ID proof such as a National ID card or Driving License for issuing the participation certificate and booking the certification exam, as per the requirements.
Simply reach out to your instructors and coordinate a class time that suits your schedule. For Data Science Training Online, all sessions will be recorded and made available for easy access, allowing you to catch up on missed content at your own convenience.
Certainly! We offer a complimentary demo class to provide you with a glimpse of the training process and give you an overview of what to expect. This will help you understand the training methodology and content before making a decision to enroll.
Absolutely! At DataMites, we have a dedicated Placement Assistance Team (PAT) that works tirelessly to provide placement support to our students. Once you successfully complete the course, our team will assist you in finding suitable job opportunities and guide you through the placement process. We strive to help you kickstart your career in the field of Data Science.
The DataMites Placement Assistance Team (PAT) is dedicated to supporting applicants in every step of their Data Science career journey. PAT offers a range of services including:
With the help of our PAT, applicants can enhance their chances of securing a successful career in the field of Data Science.
The DataMites Placement Assistance Team (PAT) offers career coaching sessions to applicants, aimed at helping them identify their roles and purpose in the corporate sector. Industry experts provide guidance on the various opportunities available in the Data Science career, giving applicants a comprehensive understanding of their options. They also learn about the potential challenges they may face as newcomers and strategies to overcome them. These sessions empower applicants to make informed decisions and navigate their career paths effectively in the Data Science field.
We encourage you to make the most of your training experience. If you need a better understanding of any topic, you can request a help session or seek additional clarification from your instructors. We are here to support your learning journey and ensure that you have a comprehensive understanding of the course material.We encourage you to make the most of your training experience. If you need a better understanding of any topic, you can request a help session or seek additional clarification from your instructors. We are here to support your learning journey and ensure that you have a comprehensive understanding of the course material.
We offer multiple payment methods to make it convenient for you. You can choose to make your payment using any of the following methods:
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.