Data Science Course In Lucknow
Become proficient in the tools, languages, and libraries used by professional data scientists while learning the fundamentals of data science with our data science course in Lucknow.
Placement Opportunities
Best Data Science Course in Lucknow get expertise in Data Science
The need for data scientists who can analyse data and convey findings to support data-driven decisions has never been higher. Data science is one of the hottest occupations of the decade. Anyone interested in a career in data science or machine learning can benefit from this professional certificate in terms of developing transferable skills.
The programme is made up of numerous online courses that will equip you with the most up-to-date tools and knowledge for the workforce, such as open source tools and libraries, Python, databases, SQL, data visualisation, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. Data science is a subject that you will learn through doing.
Infoverse Academy will provide you with best data science training in Lucknow to equip you with in demand data science skills.
About Data Science Course
Your career in data science will advance thanks to this course from Infoverse Academy will give you the best data science training. The course provides in-depth instruction in the most in-demand Data Science and Machine Learning abilities as well as practical experience with important tools and technologies, such as Python, R, Tableau, and machine learning principles. To advance your career in data science, become a data scientist by delving deeply into the complexities of data interpretation, mastering technologies like machine learning, and developing strong programming abilities.
Why InfoVerse Academy?
You have come to the right place if you are serious about a career in data science. Infoverse Academy gives you one of the top data science courses in Lucknow. In numerous MNCs in India and overseas, we have helped hundreds of data science professionals launch their careers. The necessary hand-holding is done by us until you are positioned. Our knowledgeable instructors will assist you with conceptual upskilling, assignment completion, and real-world projects.
Best Training
Our experienced teachers will make you job ready after completion of this course.
Certification
Earn professional certificate from Infoverse Academy.
Career opportunities
Learn all essential skills to land your dream job
Program Overview
30+ course modules
30+ modules to teach you every topic of data science in detail.
15+ Tools to learn
Learn in demand tools necessary for being a data scientist.
200 Hours of Learning:
200 hours of learning designed by experts.
25+ Case Studies and Live Projects
Well curated case studies and live projects to give you hands-on experience.
Video Library of recorded classes
On demand series of recorded sessions for you to watch at your comfort.
Online and Offline Training
We provide both online as well as offline learning and cover all topics.
Certifications from Infoverse Academy
Professional certificate to boost up your resume.
Internship Program
We will assist you in getting an internship after completion of course.
Career mentoring
Our mentors are available for you all the time to guide you with your career.
Resume assistance
Learn how to make a professional resume.
Job assistance
Curated team for helping you in landing a job after completion of the course.
Get Your Hands On Data Science Tools
You will be able to implement appropriate plans in the appropriate way with the support of data science tools and language . These tools are made to keep things organized and provide you the flexibility to customize things as needed.
Skills Covered In Data Science
1.
Learn python coding from scratch:
have a basic knowledge of the Python programming language. Obtain the necessary Python knowledge to advance in fields like Machine Learning and Data Science. Object-oriented programming (OOP) skills in Python should be added to your resume.
2.
Learn Data Visualization with Python:
You'll discover how to make a variety of fundamental and sophisticated graphs and charts, including: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! Additionally, you'll develop interactive dashboards that even those with no prior knowledge of data science can use to better understand the information and make more sensible decisions.
3.
Learn Machine Learning and 6 important Machine Learning Algorithms:
The K-Nearest Neighbors (KNN), decision trees, and logistic regression classification algorithms will be used as you delve into classification approaches. Additionally, you'll discover the value of clustering and its various forms, including DBSCAN, hierarchical clustering, and k-means.
4.
Fundamentals of Robotic Automation:
This course's objective is to give you a thorough understanding of RPA, including what it is, how it operates, and when to use it, as well as practical technology experience by guiding you through the creation of a straightforward robot that automates a business process using the RPA software UiPath Studio.
5.
Linear Regression and Modeling:
Using the free statistical tools R and RStudio, you will study the basic theory of linear regression in this course as well as how to fit, examine, and use regression models to look at correlations between many variables.
6.
Logistic regression:
As their name suggests, linear models use linear presumptions to relate a result to a group of relevant factors. The most crucial statistical analytic tool in the arsenal of a data scientist is regression modelling, which is a subset of linear modelling. Least squares, regression model inference, and regression analysis are all topics covered in this course. ANOVA, ANCOVA, and special situations of the regression model will also be examined. We'll look into residual analysis and variability.
7.
Learn Cluster Analysis in Data Science:
Learn the fundamentals of cluster analysis before examining a variety of common clustering approaches, algorithms, and applications. This includes density-based techniques like DBSCAN/OPTICS, hierarchical techniques like BIRCH, and partitioning techniques like k-means. Learn techniques for clustering validation and clustering quality assessment as well. View applications using cluster analysis, to finish.
8.
Learn to use SQL queries with Python:
This course will give you the fundamental knowledge needed for Data Science, including relational databases, Python, statistical analysis, SQL, and open-source tools and frameworks. These data science prerequisites are taught through practical experience with actual data science tools and real-world data sets.
Course Modules Details
You’ll gain knowledge of relational databases, statistical analysis, and big data ideas, as well as the different open source data science tools and programmes used by data scientists, such as Jupyter Notebooks, RStudio, GitHub, and SQL. To apply your newly learned skills and knowledge to real-world data sets and grasp the technique involved in solving data science problems, you’ll complete practical labs and projects.
Learn how to show data insights using visualisation platforms that are routinely used in dashboards, presentations, and other formats. Know how to prepare data for analysis, clean and organise it for analysis, and perform computations using spreadsheets, SQL, and R programming.
What an Excel Spreadsheet is, why we use them, and the most crucial keyboard keys, features, and fundamental formulas will all be covered.
Some of the initial processes in creating data visualisations using spreadsheets and dashboards are covered in this module. Create one of the many different types of charts that are available in spreadsheet programmes like Excel to start the process of constructing a story with your data. See the various spreadsheet tools, including the pivot function, dashboard creation, and other useful features, and discover how each one may change your data in a different way.
What cases and variables are, how to calculate measures of central tendency (mean, median, and mode), and dispersion will all be covered in this lesson (standard deviation and variance). We next go over how to evaluate relationships between variables, how to calculate probabilities, how to create probability distributions, and how to create sampling distributions. To comprehend how inferential statistics function, you must be aware of these things.
You’ll become an expert in fundamental spreadsheet operations, create descriptive business data metrics, and hone your data modelling skills. Along with learning about fundamental ideas in probability, such as measuring and modelling uncertainty, you’ll also apply the Linear Regression Model and different data distributions to assess and inform business decisions.
You will learn about programming languages in this chapter, using Python as an illustration. You will learn how to create basic Python programmes using variables, operators, input/output, and flow controls.
You will learn how to perform exploratory data analysis (EDA), clean and manage data, import data from various sources, and produce useful data visualisations. The next step is to create linear, multiple, polynomial regression models & pipelines and learn how to evaluate them in order to forecast future trends from data.
You’ll discover how modern computer science research led to useful systems and what systems are on the horizon. There will be coverage of cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it generated, Spark and its contemporaries, and specialised systems for graphs and arrays.
You’ll comprehend the principles more thoroughly. By the end of this lesson, you will be able to use Python and NumPy to create a logistic regression model, do a simple exploratory data analysis, and create a gradient descent algorithm from scratch.
You will examine data sets that represent sequential information in practical time series analysis, including stock prices, annual rainfall, sunspot activity, the cost of agricultural items, and more. Discover many mathematical models that can be used to explain the processes that produce these kinds of data. We also take a look at graphic representations of our data because they offer insights.
You will learn the fundamentals of writing straightforward Python programmes using the most typical structures in this session. No prior programming experience is necessary. By the end of this course, you’ll comprehend the advantages of programming in IT roles, be able to create simple Python programmes, understand how the many components of programming work together, and be able to put all of this information together to solve a challenging programming challenge.
Using libraries like Pandas, Numpy, and Scipy, do exploratory data analysis and apply analytical techniques to real-world datasets. Create Python code to format data, handle missing values, normalise it, and bin the data before cleaning and preparing it for analysis.
The fundamentals of Python 3 are covered in this chapter, including the use of iteration and conditional execution as control structures, as well as strings and lists as data structures. You’ll instruct an on-screen Turtle to create artistic images. Additionally, you’ll develop your ability to debug programmes by learning how to reason about programme executions by using reference diagrams.
The fundamentals of programming, such as data structures, conditionals, loops, variables, and functions, are introduced to the students. Students can start coding right away with the help of this chapter, which also provides an overview of the major Python writing and execution tools. Additionally, it offers practical coding activities that involve building original functions, reading and writing files, and employing frequently used data structures.
You will be introduced data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively.
The popular Python data science library pandas will be used to demonstrate data cleaning and manipulation techniques in this chapter. It will also introduce the abstraction of the Series and DataFrame as the primary data structures for data analysis and provide tutorials on how to effectively use features like groupby, merge, and pivot tables.
With Pandas and Python, you will become an expert at data analysis and manipulation. Pandas is an open-source data analysis and manipulation tool that is incredibly robust, quick, flexible, and simple to use. Anyone who wishes to master data analysis with pandas should start with this guided project, which is the first in a series of numerous guided projects.
Learn about pandas objects’ hundreds of methods and properties. With Python’s well-known pandas package, you can perform a wide range of data operations, such as grouping, pivoting, joining, and more.
Learn why pandas, the most widely used Python library in the world, is used for everything from data manipulation to data analysis in this chapter. As you extract, filter, and alter actual datasets for analysis, you’ll learn how to work with DataFrames.
You will be able to use the crucial Python module known as Matplotlib and comprehend the fundamentals of data visualisation with Python. You’ll discover how to make pie charts, pair plots, countplots, line plots, scatterplots, histograms, distribution plots, 3D graphs, and a lot more!
The learners will be able to explore fundamental data visualisation concepts in this chapter that are transferable to different languages. You will learn how to utilise Jupyter in this session to create and select the best graphs to represent your data.
This section of the course will give you a basic understanding of machine learning models (such as logistic regression, multilayer perceptrons, convolutional neural networks, and natural language processing) and show you how these models can be used to solve challenging problems in a range of fields, such as text prediction, image recognition, and medical diagnostics. Additionally, we have created practise tasks that will allow you to put these data science models into reality on actual data sets.
Understand the well-known machine learning packages NumPy and scikit-learn to create machine learning models in Python. Create and refine supervised machine learning models for binary classification and prediction problems, such as logistic regression.
Learn to evaluate accuracy of model using machine learning algorithms.
Use best practises while developing your machine learning models to ensure that they can be applied to data and tasks in the real world. Create and use tree ensemble methods, such as random forests and boosted trees, and decision trees. TensorFlow may be used to create and train a neural network for multi-class classification.
Unsupervised Learning, one of the primary categories of machine learning, is covered in this chapter. You’ll discover how to draw conclusions from data sets devoid of a goal or labelled variable. For unsupervised learning, you will learn a variety of clustering and dimension reduction strategies as well as how to choose the algorithm that best fits your data.
Learners will be prepared to enrol in more advanced courses or use AI tools and concepts to solve real-world issues by comprehending the theoretical underpinnings of a large portion of contemporary probabilistic artificial intelligence (AI). In this section, “small-scale” issues will be emphasised in order to better comprehend the principles of reinforcement learning.
The Deep Learning session is a foundational programme that will help in your comprehension of deep learning’s potential, difficulties, and effects as well as equip you to take part in the creation of cutting-edge AI technology. Building and training neural network designs including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers are covered in this chapter. You’ll also learn how to improve them using techniques like Dropout, BatchNorm, Xavier/He initialization, and more.
Course Duration
6 Months
Rs. 29499/- (GST Exclusive)
- New Batch: 6 January, 2023
- Duration: 6 Months
- Paid Internship
Job opportunities after completing a Data course in Lucknow
Recently, there has been a lot of talk around data science careers, and this buzz is not unwarranted. Data science has developed beyond simple analytics and statistics to include judgments, forecasts, and movements that affect the world. Being a master of all trades, including programmer, analyst, engineer, mathematician, statistician, and strategist, is therefore necessary for a successful career in data science. A data scientist must, above all, love data. Their insatiable curiosity drives them to create patterns, spot trends, analyse data, and find solutions to practical problems. Our data science training in Lucknow program will support you in landing your dream job as data scientist.
Recent Reviews from Students
★★★★★
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The business cases and all real-world examples helped connect the theory to practical application. Bravo and many thanks!
★★★★★
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It's excellent that the course is divided up into manageable chunk pieces. Additionally, the quizzes allow you to assess your comprehension and improve focus. I heartily urge anyone interested in learning data science to take this data science course in Lucknow.
★★★★★
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Absolutely loved this course. learned a completely new aspect of data science and its characteristics.
★★★★★
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best data science course I have ever taken, without a doubt. Everything is conveyed in a straightforward and professional manner by the teachers.
FAQs
You can participate in this course without any prior coding experience. You can begin with the Beginner module, where we will discuss the fundamentals of coding.
In reality, prior experience with data science or machine learning is not required. We will start from scratch and cover all pertinent issues.
The whole maths necessary to comprehend and use algorithms will be covered (Probability, Statistics, Linear Algebra, Calculus, Coordinate Geometry).
You’ll receive a diploma from the Postgraduate Program in Data Science and Analytics after successfully completing this course of study. Your professional credentials will gain a lot from this qualification.
We teach in offline as well as online mode.
Visit the official website of Infoverse Academy to register for this course.
Your ability to contribute to a wide range of tasks in a job in the data science sector will be enhanced by doing this Best Data Science course in India. Data Science is a discipline that is always changing, and new employment responsibilities and titles are constantly becoming available. You will be qualified for a variety of positions after completing this programme, including Data Analyst, Data Science Generalist, Data Scientist, ML Analyst, ML Engineer, ML Scientist, AI Analyst, AI Engineer, AI/ML Developer, Business Intelligence Analyst, Associate Data Scientist, Data Architect, Business Intelligence Developer, Deep Learning Engineer, Decision Scientist, Data Visualization Specialist, and many more.
With 11.5 million employment openings in the field of data science by 2026, demand is growing. Demand ensures that data positions pay well. The job’s starting salary ranges from 4 to 5 lakhs INR. However, we cannot assure you of a minimum wage.
If you have completed the Infoverse Academy’s Data Science programme successfully, our experts will work with you to find job after you have earned the professional certificate we will provide you.
