Data Science Courses – PG Certificate Program in Advanced Data Science
Data Science Course: PG Certificate Program in Advanced Data Science Program by ACLM
ACLM’s Data science course is now a been top of the mind course for learning and development. It’s known as the most demand course of the era. In the century of 2021, this emerging field has become a necessity for all businesses and evolving data driven outcomes on the entire globe.
ACLM’s team helping to each top companies looking for the best data scientist for their organization, who can result the best possible through the data.
It has been observed that the data scientist job became the fastest growing jobs around the globe.
Types of Data Science Courses
Intermediate
An Intermediate level of programs gets over within 03-06 Months durations with an internationally recognized certification.
Degree
A Degree Program of Data Science by ACLM get’s over in a 12 Months span. The best part is interview and placement support.
Post Graduate
The PG Degree in Data Science is a special 12 Months program with 03 Major projects and a Degree + Certifications.
Data Science Course: PROGRAM HIGHLIGHTS
60+ Case Studies & Assignments
Work on 60+ Case studies and Assignments with 24/7 Assignment support.
25+ Industry Relevant Projects
Get Industrial experience by working on our Industry Relevant Live Projects.
Tied-up with 150+ Companies
ExcelR has Tied up with 150+ Companies to Provide Jobs to Many Students
Job Readiness
Program
A dedicated placement cell for the participants who completed the course
Skills Covered Under Data science
Data Science Course –
Explore Our Research Centers Data science
“In the past decade, data scientists have become indispensable assets and are now ubiquitous in nearly all organizations. Their expertise in extracting valuable insights from data has transformed decision-making, innovation, and competitiveness across industries.” ACLM’s Data Science Course has a combination of important modules from various complex Data Centers.
Data Science Course:
TOOLS AND TECHNOLOGIES for Data Science
Data Science Course:
Data Science in various sector
Cybersecurity
This discipline focuses on safeguarding digital systems, networks, and data from cyber threats and attacks. ACLM’s Data Science Course has been designed to involves techniques like intrusion detection, malware analysis, and network monitoring to protect against vulnerabilities and breaches.
Cybersecurity professionals use data-driven approaches to identify and respond to security incidents, leveraging data analytics to detect anomalies and patterns that might indicate cyber threats
Financial, Business Analytics
Financial and business analytics are essential disciplines in modern organizations, focusing on using data and quantitative methods to gain insights, make informed decisions, and drive performance improvements.
Data, Media and Society
The relationship between data, media, and society is complex and multifaceted. It involves issues of media literacy, data ethics, and the regulation of data-driven technologies. Understanding this interplay is crucial for individuals, media professionals, policymakers, and society as a whole to navigate the evolving landscape of information dissemination and its societal impact.
- Do you know, what is data science?
- Do you want to derive key learning path for becoming a data scientist
- Do you want to explore some other courses too?
Data Science vs. Machine Learning
Data Science Course: Comparison between Data Science Vs. Machine Learning: Because algorithms, statistics, and analysis are all such integral parts of data science, it’s all too common for data science jobs to be conflated with machine learning skills. In reality, machine learning is one of many key capabilities for data scientists. With machine learning, systems take datasets and run them through models to refine algorithms and generate more effective results. Data science utilizes these algorithms and the benefits of machine learning but isn’t always an automated process. Rather, data science is a broader spectrum that includes data integration, architecture, visualization, business intelligence, decision-making, predictive analytics, and more. As you build out your data science training program, don’t stop short with machine learning courses because they’ve grown so popular. Consider the full spectrum of data science functions.
Preliminary Key Skills / Eligibility for Data Science Courses
- The data science courses are require soft skills and technical skill to success in this field
- You must have strong and good command over excel, VBA, SQL, Tableau, Python
- You should also be aware about statistical methods to adopt in your models
- Graduates (10+2+3) or Diploma Holders (only 10+2+3) from a recognised university in any discipline with a minimum of 1 year of work experience (after graduation) as on December 30, 2023.
What you’ll learn in Data Science course
- Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn
- Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
Data Science Course Topics & Duration
Excel + Advanced Excel + VBA – 4 Weeks
- Advanced Excel – 4 Weeks
- VBA Programming – 2 Weeks
- SQL Programming – 1 Week
- SAS Base Programming – 2 Week
- Python Programming – 2 Weeks
- Python Project – 2 Weeks
- Statistics – 10 Days
- Machine Learning with Python – 7 Weeks
- Machine Learning Project
- Pandas Data Science – 1 Week
- Pandas Project – 1 Week
- Data Visualization with Tableau – 1 Week
- Placement Preparation & Capstone Project
SQL / MYSQL Database – 03 Weeks
- Conceptual Design with UML Translation
- Database, Table, Procedure and Trigger Creation
- Learn about various DML , DDL & TCL Controls
- join operators in SQL
- Learn about intermediate SQL queries
- Learn about advanced SQL queries
- Various views in SQL with Pparametric Procedures
- Microsoft Excel (2007 / 2010 / 2013 / 2016 / 2019/ Office 365)
- Microsoft Access (2007 / 2010 / 2013 / 2016 / 2019)
- Python with Pandas and Matplotlib
- Python NumPy, SciPy, XLRD & Jupiter
- Python Pandas Framework to handle stats
- SQL Queries, Procedures & Triggers
- SQL Join (Left, Outer & Inner) with DDL, DML, DCL & TCL
- VBA (EXCEL + ACCESS)
- Dynamic Dashboard Preparation
- Presenting with Power Point
- Story and Dashboard Creating using Tableau and Matplotlib
- 100+ assignments on Excel, VBA , SQL & MIS(Charts)
- Notes & Exercises on Advance Excel and its Properties
- Command on more than 250+ formulas
- All Excel & VBA ribbon with VBA Forms + Scenarios
- Graphs & Charts with Pivot Tables in detailed form
- Test and Mock Interview Sessions for interviews
- Exposure on Real time work enviroment
- ODBC with Excel, Set up various data sources(s)
Python Data Analytics & Data Science
- Learn about previous Python versions. Similarities and differences
- Data Types in Python, learn about lists, tuples, dictionary and many more
- Creating variables, declaring global and local variables and reusing variables
- Creating mixed data sets with various data types in python
- Generating sequential and random number series in numerics, date, time
- Learn to generate binary, hexacodes using python with various exercises
- Learn to create python modules, classes
- Creating and implementing existing python collections
- Design and create python parametric and non-parametric functions
- Looping and Data Flow in Python, learn to play with various loops
- Exception handling in python, learn to use {try} {except} block in python
- Practice sessions with the help of assignments, notes and assessment
- File handling in python with various unorganized data in python like csv, txt, tbdl and many more
- Data handling with the help of Pandas python framework
- Learn to installing and un-installing external python libraries
- Using external libraries like pandas, dataframe(df), numpy skypy, ndarrays and xlrd
- Identifying bad data / missing data and data cleaning technique with the help of python
- Creating Graphs, Charts, Maps in Python for GUI representation
- Merging various data sets in python
- Data statistics in python: learn linear regression, data modelling, differentiation, T-Test, F-Test, Sampling, Co-relations and many more
- Machine Learning (ML) and various other approaches to statistics.
- Power BI Job Roles in Real-time
- Power BI Data Analyst Job Roles
- Business Analyst – Job Roles
- Power BI Developer – Job Roles
- Power BI for Data Scientists
- Comparing MSBI and Power BI
- Comparing Tableau and Power BI
- MCSA 70-778, MCSA 70-779 Exam
- Types of Reports in Real-World
- Interactive & Paginated Reports
- Analytical & Mobile Reports
- Data Sources Types in Power BI
- Power BI Licensing Plans – Types
- Power BI Training : Lab Plan
- Power BI Dev & Prod Environments
- Understanding the Power BI Tools
- Installing Power BI & Connecting to Data
- The “Locale” used in the curriculum
- Working with the query Editor
- Working with the data model and creating a visualization
Topics Covered Under Python Data Analytics
- Comparison of Python with other languages and prior history
- Strings and Numbers in python
- Data Structures in Python using lists, tuples & Dictionary
- The powerful Datetime Module in Python
- Handling Data and Memory in Python
- Various data flow control in python (While, Do Loop, For and For Each)
- Usage of Functions in Python (Parametric Vs. Non-parametric)
- Error or Exception Handling in Python
- Sorting and Filtering Raw Data using Pandas
- Defining your own variable
- Data Frames and Series
- Accessing elements by Index
- Reading and Writing
- Agreegating and Grouping in Pandas
- Pivoting tables in Pandas
- Time Series Analysis in Pandas
- Visualization in Pandas
- Statistical Analysis using Pandas
- Assignments 1: on Pandas Data Handling
- Assignment 2: on Pandas Data Framing and Pivoting
- Assignemnt 3: Pandas Stats Problem solving exercises
Handling data: Query and Questionnaire
- Assignment 1 on Binary, Decimal and Hexacodes
- Assignment 2: Handling inputs through looping, multiple question
- Assignment 3: String handling in python. The interactive 32 questions in python
- Correcting bad Data in Python with various methods
- Installing and activating NumPy
- Handling 1D, 2D and ND Array Data
- Using Axis Parameters
- Deploying NumPy Data
- Handling Bad / Missing data in NumPy
Projects you’ll do under ACLM’s Data Science Course
Through rigorous exercises, hands-on projects, assessments, hackathon and a personalized Capstone project, this program will prepare you to start your career in Data Analytics and Machine Learning/Data Science at A-list firms and startups.
Certifications related to Data Science Course:
Upon successfully completing this program, you’ll earn a Diploma Program in Data Analytics and Machine Learning Certificate. This Program in Data Analytics and Machine Learning Course will add considerable value to your professional credentials.
Participants who successfully complete all evaluation components with minimum pass marks and meet the requisite 75% minimum attendance criteria will be awarded a Certificate of Completion from ACLM. Participants who are unable to clear the evaluation criteria but have the requisite attendance will be awarded a Participation Certificate.
