Data Science
The Data science Course in Nigeria is one of the most promising and in-demand job pathways for competent individuals, and it is only getting better. Successful data professionals recognise that they must go beyond the typical abilities of evaluating vast volumes of data, data mining, and programming.
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- Weekends: 2 Batches
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About Data Science
To unearth meaningful knowledge for their organisations, data scientists must grasp the whole data science life cycle and be flexible and sophisticated enough to maximise returns at each stage of the process.
Emergence of Data scientists
The term "data scientist" was coined in 2008, when businesses recognised the need for data experts capable of organising and analysing vast amounts of data. Hal Varian, Google's top economist and UC Berkeley professor of information sciences, business, and economics, foresaw the significance of adjusting to technology's influence and reconfiguring diverse businesses in a 2009 McKinsey&Company piece.
Importance of Data Scientists
Effective data scientists can develop relevant questions, acquire data from a variety of diverse data sources, organise the information, convert the results into solutions, and present their findings in a way that favourably influences strategic decisions. Since these talents are required in practically all areas, competent data analysts are becoming highly attractive to businesses.
What Is the Role of a Data Scientist?
Data scientists have become indispensable assets in the last decade, and they are now found in practically every firm. These professionals are well-rounded, data-driven individuals with high-level technical abilities who can construct complicated quantitative algorithms to organise and synthesise vast amounts of data used to answer questions and drive strategy in their business. This Data Science training in Nigeria combined with the communication and leadership skills required to provide tangible results to numerous stakeholders within an organisation or business.
What Are the Benefits of Becoming a Data Scientist?
Since 2016, Glassdoor has named data scientist as one of America's top three jobs.
4 Large tech companies aren't the only ones in need of data scientists as more data becomes available. A shortage of skilled people available to fill open positions is posing a challenge to the expanding need for data science course in Nigeria across sectors, large and small.
In the coming years, the demand for data scientists is not expected to decrease. LinkedIn named data scientist as one of the most promising careers in 2021, as well as numerous data-science-related talents as the most in-demand by businesses. 6
The numbers presented below show that there is a substantial and growing demand for data scientists.
Where Do You Fit in Data Science Classes in Nigeria?
Data is pervasive and expansive. A range of phrases relating to data mining, cleaning, analysing, and interpreting are frequently used interchangeably, but they can really involve diverse skill sets and data complexity.
Data Scientist
Data scientists investigate which questions must be answered and where the relevant data may be found. They have commercial acumen and analytical skills, as well as the ability to extract, clean, and display data. Data scientists are employed by businesses to collect, manage, and analyse enormous amounts of unstructured data. The results are then synthesised and distributed to key stakeholders in order to influence strategic decision-making in the company.
Programming capabilities (SAS, R, Python), statistical and mathematical abilities, storytelling and data visualisation, Hadoop, SQL, and machine learning are required.
Analyst of Data
Data analysts serve as a link between data scientists and business analysts. They are given questions from an organisation that need to be answered, and then they arrange and analyse data to obtain results that correspond with high-level corporate strategy. Data Science courses in Nigeria analysts must translate technical analyses into qualitative action items and successfully communicate their findings to a wide range of stakeholders.
Programming abilities
(SAS, R, Python), statistical and mathematical knowledge, data manipulation, and data visualisation are required.
vProspects for a Career in Data Science and Salary Opportunities
Data science specialists are rewarded for their highly technical skill set with competitive salaries and excellent career possibilities at both large and small businesses across most industries. With nearly 6,000 vacant opportunities advertised on Glassdoor, data science professionals with the necessary expertise and education may make their mark in some of the world's most forward-thinking firms.
Lifecycle of Data Science
The lifetime of data science classes in Nigeria is divided into five stages, each with its own set of tasks:
Data Acquisition, Data Entry, Signal Reception, and Data Extraction are all aspects of data capture. Gathering raw organised and unstructured data is part of this stage.
Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture must all be kept up to date. This stage entails converting raw data into usable form.
Data Mining, Clustering/Classification, Data Modeling, and Data Summarization are the processes involved in this process. Data scientists assess the generated data for patterns, ranges, and biases to determine its utility in predictive analysis.
Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, and Qualitative Analysis This is where the lifespan really becomes interesting. This stage entails conducting numerous data analyses.
Data Reporting and Data Communication.
Online Classes
It is an excellent opportunity for students to gain admission to some of the greatest training schools and courses. Technology is advancing, and any of it can improve students' professional opportunities and skills. The Online Data Science Course in Nigeria is in high demand and highly recommended. SevenMentor & Training Pvt. Ltd. is a training institute where students can learn about various cutting-edge technology topics. Students can easily be successful in their careers and flourish throughout their lives. Our instructors are completely focused on the performance of their students. Our complete assistance can encourage students to be positioned in any of the greatest companies.
Course Eligibility
- Freshers
- BE/ Bsc Candidate
- Any Engineers
- Any Graduate
- Any Post-Graduate
- Working Professionals
Syllabus of Data Science
- 1. Installation Of Vmware
- 2. MYSQL Database
- 3. Core Java
- 1.1 Types of Variable
- 1.2 Types of Datatype
- 1.3 Types of Modifiers
- 1.4 Types of constructors
- 1.5 Introduction to OOPS concept
- 1.6 Types of OOPS concept
- 4. Advance Java
- 1.1 Introduction to Java Server Pages
- 1.2 Introduction to Servlet
- 1.3 Introduction to Java Database Connectivity
- 1.4 How to create Login Page
- 1.5 How to create Register Page
- 5. Bigdata
- 1.1 Introduction to Big Data
- 1.2 Characteristics of Big Data
- 1.3 Big data examples
- 6. Hadoop
- i) BigData Inroduction,Hadoop Introduction and HDFS Introduction
- 1.1. Hadoop Architecture
- 1.2. Installing Ubuntu with Java on VM Workstation 11
- 1.3. Hadoop Versioning and Configuration
- 1.4. Single Node Hadoop installation on Ubuntu
- 1.5. Multi Node Hadoop installation on Ubuntu
- 1.6. Hadoop commands
- Cluster architecture and block placement
- 1.8. Modes in Hadoop
- Local Mode
- Pseudo Distributed Mode
- Fully Distributed Mode
- 1.9. Hadoop components
- Master components(Name Node, Secondary Name Node, Job Tracker)
- Slave components(Job tracker, Task tracker)
- 1.10. Task Instance
- 1.11. Hadoop HDFS Commands
- 1.12. HDFS Access
- Java Approach
- ii) MapReduce Introduction
- 1.1 Understanding Map Reduce Framework
- 1.2 What is MapReduceBase?
- 1.3 Mapper Class and its Methods
- 1.4 What is Partitioner and types
- 1.5 Relationship between Input Splits and HDFS Blocks
- 1.6 MapReduce: Combiner & Partitioner
- 1.7 Hadoop specific Data types
- 1.8 Working on Unstructured Data Analytics
- 1.9 Types of Mappers and Reducers
- 1.10 WordCount Example
- 1.11 Developing Map-Reduce Program using Eclipse
- 1.12 Analysing dataset using Map-Reduce
- 11.13 Running Map-Reduce in Local Mode.
- 1.14 MapReduce Internals -1 (In Detail) :
- How MapReduce Works
- Anatomy of MapReduce Job (MR-1)
- Submission & Initialization of MapReduce Job (What Happen ?)
- Assigning & Execution of Tasks
- Monitoring & Progress of MapReduce Job
- Completion of Job
- Handling of MapReduce Job
- Task Failure
- TaskTracker Failure
- JobTracker Failure
- 1.15 Advanced Topic for MapReduce (Performance and Optimization) :
- Job Sceduling
- In Depth Shuffle and Sorting
- 1.16 Speculative Execution
- 1.17 Output Committers
- 1.18 JVM Reuse in MR1
- 1.19 Configuration and Performance Tuning
- 1.20 Advanced MapReduce Algorithm :
- 1.21 File Based Data Structure
- Sequence File
- MapFile
- 1.22 Default Sorting In MapReduce
- Data Filtering (Map-only jobs)
- Partial Sorting
- 1.23 Data Lookup Stratgies
- In MapFiles
- 1.24 Sorting Algorithm
- Total Sort (Globally Sorted Data)
- InputSampler
- Secondary Sort
- 1.25 MapReduce DataTypes and Formats :
- 1.26 Serialization In Hadoop
- 1.27 Hadoop Writable and Comparable
- 1.28 Hadoop RawComparator and Custom Writable
- 1.29 MapReduce Types and Formats
- 1.30 Understand Difference Between Block and InputSplit
- 1.31 Role of RecordReader
- 1.32 FileInputFormat
- 1.33 ComineFileInputFormat and Processing whole file Single Mapper
- 1.34 Each input File as a record
- 1.35 Text/KeyValue/NLine InputFormat
- 1.36 BinaryInput processing
- 1.37 MultipleInputs Format
- 1.38 DatabaseInput and Output
- 1.39 Text/Biinary/Multiple/Lazy OutputFormat MapReduce Types
- iii)TOOLS:
- 1.1 Apache Sqoop
- Sqoop Tutorial
- How does Sqoop Work
- Sqoop JDBCDriver and Connectors
- Sqoop Importing Data
- Various Options to Import Data
- Table Import
- Binary Data Import
- SpeedUp the Import
- Filtering Import
- Full DataBase Import Introduction to Sqoope
- 1.2 Apache Hive
- 1.2 Apache Hive
- What is Hive ?
- Architecture of Hive
- Hive Services
- Hive Clients
- How Hive Differs from Traditional RDBMS
- Introduction to HiveQL
- Data Types and File Formats in Hive
- File Encoding
- Common problems while working with Hive
- Introduction to HiveQL
- Managed and External Tables
- Understand Storage Formats
- Querying Data
- 1.3 Apache Pig :
- What is Pig ?
- Introduction to Pig Data Flow Engine
- Pig and MapReduce in Detail
- When should Pig Used ?
- Pig and Hadoop Cluster
- Pig Interpreter and MapReduce
- Pig Relations and Data Types
- PigLatin Example in Detail
- Debugging and Generating Example in Apache Pig
- 1.4 HBase:
- Fundamentals of HBase
- Usage Scenerio of HBase
- Use of HBase in Search Engine
- HBase DataModel
- Table and Row
- Column Family and Column Qualifier
- Cell and its Versioning
- Regions and Region Server
- HBase Designing Tables
- HBase Data Coordinates
- Versions and HBase Operation
- Get/Scan
- Put
- Delete
- 1.5 Apache Flume:
- Flume Architecture
- Installation of Flume
- Apache Flume Dataflow
- Apache Flume Environment
- Fetching Twitter Data
- 1.6 Apache Kafka:
- Introduction to Kafka
- Cluster Architecture
- Installation of kafka
- Work Flow
- Basic Operations
- Real time application(Twitter)
- 4)HADOOP ADMIN:
- Introduction to Big Data and Hadoop
- Types Of Data
- Characteristics Of Big Data
- Hadoop And Traditional Rdbms
- Hadoop Core Services
- Hadoop single node cluster(HADOOP-1.2.1)
- Tools installation for hadoop1x.
- Sqoop,Hive,Pig,Hbase,Zookeeper.
- Analyze the cluster using
- a)NameNode UI
- b)JobTracker UI
- SettingUp Replication Factor
- Hadoop Distributed File System:
- Introduction to Hadoop Distributed File System
- Goals of HDFS
- HDFS Architecture
- Design of HDFS
- Hadoop Storage Mechanism
- Measures of Capacity Execution
- HDFS Commands
- The MapReduce Framework:
- Understanding MapReduce
- The Map and Reduce Phase
- WordCount in MapReduce
- Running MapReduce Job
- WordCount in MapReduce
- Running MapReduce Job
- Hadoop single node Cluster
- Hadoop single node Cluster Setup :
- Hadoop single node cluster(HADOOP-2.7.3)
- Tools installation for hadoop2x
- Sqoop,Hive,Pig,Hbase,Zookeeper
- Hadoop single node Cluster Setup :
- Hadoop single node cluster(HADOOP-2.7.3)
- Tools installation for hadoop2x
- Sqoop,Hive,Pig,Hbase,Zookeeper.
- Yarn:
- Introduction to YARN
- Need for YARN
- YARN Architecture
- YARN Installation and Configuration
- Hadoop Multinode cluster setup:
- hadoop multinode cluster
- Checking HDFS Status
- Breaking the cluster
- Copying Data Between Clusters
- Adding and Removing Cluster Node
- Name Node Metadata Backup
- Cluster Upgrading
- Hadoop ecosystem:
- Sqoop
- Hive
- Pig
- HBase
- zookeeper
- >7. MONGODB
- 8. SCALA
- 1.1 Introduction to scala
- 1.2 Programming writing Modes i.e. Interactive Mode,Script Mode
- 1.3 Types of Variable
- 1.4 Types of Datatype
- 1.5 Function Declaration
- 1.6 OOPS concepts
- 9. APACHE SPARK
- 1.1 Introduction to Spark
- 1.2 Spark Installation
- 1.3 Spark Architecture
- 1.4 Spark SQL
- Dataframes: RDDs + Tables
- Dataframes and Spark SQL
- 1.5 Spark Streaming
- Introduction to streaming
- Implement stream processing in Spark using Dstreams
- Stateful transformations using sliding windows
- 1.6 Introduction to Machine Learning
- 1.7 Introduction to Graphx
- Hadoop ecosystem:
- Sqoop
- Hive
- Pig
- HBase
- zookeeper
- 10. TABLEAU
- 11. DATAIKU
- 12. Product Based Web Application Demo based on java(EcommerceApplication)
- 13. Data deduplication Project
- 14. PYTHON
- 1.Introduction to Python
- What is Python and history of Python?
- Unique features of Python
- Python-2 and Python-3 differences
- Install Python and Environment Setup
- First Python Program
- Python Identifiers, Keywords and Indentation
- Comments and document interlude in Python
- Command line arguments
- Getting User Input
- Python Data Types
- What are variables?
- Python Core objects and Functions
- Number and Maths
- Week 1 Assignments
- 2.List, Ranges & Tuples in Python
- Introduction
- Lists in Python
- More About Lists
- Understanding Iterators
- Generators , Comprehensions and Lambda Expressions
- Introduction
- Generators and Yield
- Next and Ranges
- Understanding and using Ranges
- More About Ranges
- Ordered Sets with tuples
- 3.Python Dictionaries and Sets
- Introduction to the section
- Python Dictionaries
- More on Dictionaries
- Sets
- Python Sets Examples
- 4. Python built in function
- Python user defined functions
- Python packages functions
- Defining and calling Function
- The anonymous Functions
- Loops and statement in Python
- Python Modules & Packages
- 5.Python Object Oriented
- Overview of OOP
- Creating Classes and Objects
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- 6. Python Object Oriented
- Overview of OOP
- Creating Classes and Objects
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- 7. Python Exceptions Handling
- What is Exception?
- Handling an exception
- try….except…else
- try-finally clause
- Argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions
- 8. Python Regular Expressions
- What are regular expressions?
- The match Function
- The search Function
- Matching vs searching
- Search and Replace
- Extended Regular Expressions
- Wildcard
- 9. Python Multithreaded Programming
- What is multithreading?
- Starting a New Thread
- The Threading Module
- Synchronizing Threads
- Multithreaded Priority Queue
- Python Spreadsheet Interfaces
- Python XML interfaces
- 10. Using Databases in Python
- Python MySQL Database Access
- Install the MySQLdb and other Packages
- Create Database Connection
- CREATE, INSERT, READ, UPDATE and DELETE Operation
- DML and DDL Oepration with Databases
- Performing Transactions
- Handling Database Errors
- Web Scraping in Python
- 11.Python For Data Analysis –
- Numpy:
- Introduction to numpy
- Creating arrays
- Using arrays and Scalars
- Indexing Arrays
- Array Transposition
- Universal Array Function
- Array Processing
- Arrary Input and Output
- 12. Pandas:
- What is pandas?
- Where it is used?
- Series in pandas
- Index objects
- Reindex
- Drop Entry
- Selecting Entries
- Data Alignment
- Rank and Sort
- Summary Statics
- Missing Data
- Index Heirarchy
- 13. Matplotlib: Python For Data Visualization
- 14. Welcome to the Data Visualiztion Section
- 15. Introduction to Matplotlib
- 16. Django Web Framework in Python
- 17. Introduction to Django and Full Stack Web Development
- 15. R Programming
- 1.1 Introduction to R
- 1.2 Installation of R
- 1.3 Types of Datatype
- 1.4 Types of Variables
- 1.5 Types of Operators
- 1.6 Types of Loops
- 1.7 Function Declaration
- 1.8 R Data Interface
- 1.9 R Charts and Graphs
- 1.10 R statistics
- 16) Advance Tool for Analysis
- 1.1 git
- 1.2 nmpy
- 1.3 scipy
- 1.4 github
- 1.5 matplotlib
- 1.6 Pandas
- 1.7 PyQT
- 1.8Theano
- 1.9 Tkinter
- 1.10 Scikit-learn
- 1.11 NPL
- 17. Algorithm
- 1.naive bayes
- 2.Linear Regression
- 3.K-nn
- 4.C-nn
Trainer Profile of Data Science
Our Trainers explains concepts in very basic and easy to understand language, so the students can learn in a very effective way. We provide students, complete freedom to explore the subject. We teach you concepts based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates can learn in our one to one coaching sessions and are free to ask any questions at any time.
- Certified Professionals with more than 8+ Years of Experience
- Trained more than 2000+ students in a year
- Strong Theoretical & Practical Knowledge in their domains
- Expert level Subject Knowledge and fully up-to-date on real-world industry applications
Data Science Exams & Certification
SevenMentor Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.
Our certification at SevenMentor is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.
Proficiency After Training
- Learn all new aspects of Data Science.
- You will have a good understanding of Data Science Algorithms.
- You will be able to work on real time projects.
- You will be able to work on file formats on different data.
Key Features
Skill Level
Beginner, Intermediate, Advance
We are providing Training to the needs from Beginners level to Experts level.
Course Duration
90 Hours
Course will be 90 hrs to 110 hrs duration with real-time projects and covers both teaching and practical sessions.
Total Learners
2000+ Learners
We have already finished 100+ Batches with 100% course completion record.
Frequently Asked Questions
Batch Schedule
DATE | COURSE | TRAINING TYPE | BATCH | CITY | REGISTER |
---|---|---|---|---|---|
16/12/2024 |
Data Science |
Online | Regular Batch (Mon-Sat) | Nigeria | Book Now |
17/12/2024 |
Data Science |
Online | Regular Batch (Mon-Sat) | Nigeria | Book Now |
21/12/2024 |
Data Science |
Online | Weekend Batch (Sat-Sun) | Nigeria | Book Now |
21/12/2024 |
Data Science |
Online | Weekend Batch (Sat-Sun) | Nigeria | Book Now |
Students Reviews
During lockdown, it was a fantastic online training. Knowing the fundamentals of Python was beneficial. It motivated and inspired me to pursue a profession in data science. Thank you for sharing your expertise.
- Oshika Verma
All of the courses were interesting, lively, and beneficial in terms of efficient communication.
- Nandani Gonde
One of the top locations to study Data science training. Good experience allows you to gain more knowledge and clear all of your doubts.
- Markesh Sarvadkar
Course video & Images
Corporate Training
Employees can improve their skill set by enrolling in a corporate Data Science course in Nigeria through SevenMentor & Training Pvt. Ltd. They can improve their existing job performance by improving their practical abilities and broadening their knowledge. The productivity of a business is critical. Skilled workers can boost a company's productivity. Our lecturers are subject matter experts in a wide range of disciplines. Employee training can benefit from this and help you advance in your career.
Our Placement Process
Eligibility Criteria
Placements Training
Interview Q & A
Resume Preparation
Aptitude Test
Mock Interviews
Scheduling Interviews
Job Placement
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