Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.
Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.
This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
3 Months FREE Access to all our E-learning courses when you buy any course with us
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
In this module you will learn how to install Python distribution - Anaconda, basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.
Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.
You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.
Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.
Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
Data Science is a blend of various tools, algorithms, and machine learning principles to analyze and manipulate data with the goal to discover hidden patterns from the raw data. Data Scientist not only does the exploratory analysis to discover insights from data but also uses various advanced machine learning algorithms to predict the future. The main focus is to turn raw data into valuable business information. Data scientists must possess a combination of analytical, statistical, machine learning, data extracting skills, as well as experience with algorithms and coding. In business, these data-driven decisions drawn by data scientists can ultimately lead to increased profitability and improved operational efficiency, workflows, and business performance. In customer-facing organizations, data science helps identify and refine target audiences and in providing better customer experience.
Data Scientists are one of the most promising jobs right now. According to the salaries, opportunities, job openings, etc., the market for Data Science is only going to expand. In Los Angeles, CA, organizations like Amazon Web Services, Sony Pictures Entertainment, UCLA Health, Beyond Limits, Meredith Corporation, Cybernetic Search, Meredith Corporation, AvantStay, Munchkin Inc., NEOGOV, etc. are continuously searching for Data Scientists to help optimize their business process. The reasons for the popularity of Data Science as a career choice are as follows:
Thus, owing to the introduction of new and efficient tools every now and then, it is important to have skilled people to work with these tools. Therefore, there is a huge demand for skilled data scientists. The applications of data science in almost every sector are increasing and are predicted to increase in the future as well. This leads to an increase in the demand and a consequent increase in salary for data scientists.
Besides its financial and economic aspects, data science comes with many exciting aspects. It is a good choice for people who are inquisitive about new things for it has a wide scope for creativity and imagination. It offers a huge space for exploration, and the deeper one dives into it, newer opportunities will unravel for him.
Los Angeles, California is home to Institutes like University of California that offers Master’s in Data Science. This degree can help you understand the basic concepts of Data Science and learn about all the technical skills required to become a Data Scientist. The top skills that are needed to become a data scientist include the following:
1. Basic tools: You must have a knowledge of statistical programming like R, or Python. Solving a problem in data science involves data preprocessing, data preservation, analysis, visualization, and predictions. Python has dedicated libraries such as – Pandas, Numpy, Matplotlib, SciPy, sci-kit-learn, etc. in order to perform these functions. In addition to these, advanced Python libraries such as Tensorflow, Pytorch, and Keras provide Deep Learning tools for Data Scientists. R ideal for not just statistical analysis but also for neural networks. In order to be a proficient Data Scientist, it is necessary to extract and operate on data from the database. Therefore, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax and variety of implementations.
2. Statistics: Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:
3. Software engineering: Data scientists can gain huge benefits by learning concepts from the field of software engineering. It allows them to more easily reutilize their code and algorithms, and share it with collaborators. The important concepts include:
4. Machine Learning: Machine Learning is a subset of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience that is, the data collected over time and analyzed, without being explicitly programmed. It focuses on making future predictions from data available from past experiences. The data scientist feeds good quality data and then train our machines by building machine learning models using the data and different algorithms which depends on what type of data do we have and what kind of task we are trying to automate. Some machine learning methods are as follows:
5. Data Cleaning: Altering and filtering data in such a way that it makes sense is called data cleaning. The general sequential steps to follow are given below:
The tools available to help you with data cleaning are as follows:
6. Data Munging: Data munging, also called data wrangling is the process of mapping the raw data into another format to make it more appropriate and valuable for a variety of downstream purposes such as analytics. The purpose of data wrangling is as follows:
The tools available to perform data munging are as follows:
7. Data visualization: Data visualization methods are an important part of analytics which helps to quickly understand complex data. The process involves the creation of graphical representations of information by utilizing complex sets of numerical or factual figures. The essential data visualization techniques are as follows:
Common types of data visualization are as follows:
8. Unstructured Data: Unstructured data refers to data that does not follow any order like spreadsheet pages, database tables or other linear or ordered data sets and hence does not fit into the row and column structure of a relational database. Non-textual unstructured data such as MP3 audio files, JPEG images, and Flash video files, etc. and textual data including Word documents, PowerPoint presentations, instant messages, collaboration software, documents, books, social media posts, and medical records are all examples of unstructured data. This has led to a demand for new skills to handle such data, like NoSQL. Some of the tools for the analysis of unstructured data are as follows:
Below are the top 5 behavioral traits of a successful Data Scientist -
A data scientist must generate a set of alternative interventions to achieve the end goal. He/She should evaluate the best solutions and implement a plan accordingly.
Los Angeles, CA is the hub for several corporations that have now realized the potential of Data Science and are now searching for data scientists to help them harness this potential. Examples of such organizations include Dataiku, Brillio, Internet Brands, Snapchat, Cybernetic Search, Cedars-Sinai, Zest Finance, Riot Games, Ranker, etc.
A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:
1. Handsome salary: The trend of salaries of data scientists in Los Angeles, CA is: The median salary for a Data Scientist in Los Angeles is $128,189. The average additional cash compensation for a Data Scientist is $16,000 in Los Angeles. The average total compensation for a Data Scientist in Los Angeles, CA is $144,189. Based on your skill set and experience, you can demand higher salaries. The highest paying companies are Facebook, IBM, Accenture, Facebook, Airbnb, and Capital One. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. Data scientists need to have a versatile skill set in their profile as Data Science is a vast domain. Experts have predicted that 40 zettabytes of data will be in existence by 2020.
2. Abundance of positions: There are very few people who have the required skill-set to qualify as a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors. Therefore, Data Science is a vastly abundant field and has a lot of opportunities. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. The various roles offered for a data scientist are as follows:
3. Data Science is versatile: Data Science is a very versatile field with numerous applications in health-care, banking, consultancy services, and e-commerce industries. Data Scientists not only analyze the data but also improve its quality. Data Science deals with enriching data and making it better for their company. They help industries to automate redundant tasks. Companies are using historical data trend patterns to train machines in order to perform repetitive tasks. This makes data science a versatile career option and you get to work on various domains. There are different types of Data Scientists working in different fields:
4. Freedom to work: Data Science is an ever-evolving technology. You are not bound to work for a particular industry. Data Scientist is something that has huge potential. This leads to multiple openings for various roles in all sorts of industries. Recently, the government has also come up with many openings for Data Scientists. You get to learn a lot like technology advances. There is no hard and fast rule to work, no strict path to follow, you must possess the creativity to think beyond the conventional way. You get the freedom to work your own way and put your knowledge in different ways to come up with a solution.
5. Network: Due to the ever-increasing progress in the field of data science, many conferences and workshops are organized around the globe. These workshops invite notable expertise and top leaders from various industries to share their ideas and knowledge with others on the latest and upcoming innovations. Many researchers get to present their work. It provides an opportunity for a data scientist to connect with other people from this field and create a strong network. They can be found hanging out with C-level executives. This helps you to get a referral for better roles that you might be seeking. Also, you get to learn the operating process and work culture in different industries. It opens a door to variations of roles and work. You also get to understand your interests as well.
Below is the list of top business skills needed to become a data scientist:
1. Analytic Problem-Solving – You must be able to apply your problem-solving skills to derive a conclusion. Decision making requires critical thinking and analytical solutions. Problem-solving skills are required for the following:
2. Communication Skills – Data insights are usually presented in the form of tables, charts, or any other concise forms which should be elaborated and explained. Some of the important areas in Data Science where communication skills are important are Storytelling and Data Visualization. Data Scientists need to convey ideas and solutions to the stakeholders. It is important to have good body language and confident skills. A data scientist should have good communication skills to translate the ideas in a way understandable by other people. The various aspects of good communication skills are as follows:
3. Business acumen – The main goal of a data scientist is to translate business problems into data science solutions through the implementation of data science skills. Therefore, it is important to understand the business requirements of the organization you are working with. You must understand how your solutions affect the business on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. This lets you categorize the problems on the grounds of priority.
4. Data inquisitiveness – Curiosity is key towards acquiring mastery of any quantitative field. Data Science requires someone with expertise and knowledge. One must have a curiosity to learn more and experiment with data. A data scientist should always be inquisitive and should know when and where to ask questions. He/ She should always be ready to learn new things and accept challenges. You must update yourself with articles, blogs, new updates in programming languages, tools, etc.
Below are the best ways to brush up your data science skills for data scientist jobs:
Data has become a very important part of our lives. It is generated every day and this has increased the demand of Data Scientists. It is their responsibility to mine the data to gain insights that can help the organization make crucial marketing decisions and optimize their business processes. In Los Angeles, organizations that are currently hiring Data Scientist are Amazon Web Services, Sony Pictures Entertainment, UCLA Health, Beyond Limits, Meredith Corporation, Cybernetic Search, Meredith Corporation, AvantStay, Munchkin Inc., NEOGOV, Dataiku, Brillio, Internet Brands, Snapchat, Cybernetic Search, Cedars-Sinai, Zest Finance, Riot Games, Ranker, etc.
To start working on Data Sets and to practice your Data Science skills, you can take up projects to work on. You can do your hands on in datasets available online. You can categorize the datasets into the following three levels:
Below are the right steps to becoming a successful data scientist:
1. Choose an academic path: A Bachelor’s degree in Computer Science, Statistics, mathematics, etc., or related field is important. More and more data scientists are opting for a master's degree and Ph.D. Higher degrees helps to gain Proficiency in Data Management Technologies. You can go for online courses to add skills to your profile. Some of the certifications available are as follows:
2. Mathematics and statistics: A solid understanding of multivariable calculus and linear algebra is a must for a data scientist. Concepts of probability are the backbone of data science. It forms the foundation of many data analysis techniques. It is important for data scientists to be proficient in math since it simplifies writing algorithms. If you want to become a proficient Data Scientist, then you must be proficient in these topics:
Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:
3. Fundamentals: Before diving into the depths of Data Science, you must start with the fundamental concepts. You must master the basics to build a strong foundation for future learning. Below are some of the ideas that you can incorporate:
4. Specializations: Many data scientists will be heavily specialized in business, often specific segments of the economy or business-related fields like marketing or pricing. The need of the companies varies according to their objectives. You must choose a career path to follow and get proficient in one or more technologies related to data scientists. You can go for certifications, boot camps, and online courses. Having worked on related projects will help you upgrade your profile.
5. Apply for jobs: There are several openings every now and then by companies for various posts related to data science. You need to keep yourself updated on these notifications and prepare yourselves for the interviews.
Los Angeles, CA is home to many leading institutes.A degree from University of Southern California and the University of California can help you get expertise in technical skills of Data Science. Also, it will help improve your CV.
About 88% of data scientists have a Master's degree while about 46% have a Ph.D. degree. A degree is very important because of the following –
Los Angeles, CA is home to the University of Southern California and is renowned for its data science degree. A Master’s degree is not necessary to become a Data Scientist. But if you possess it, it is an added benefit and increases your chances of getting hired. The advantages of having a Master’s degree are as follows:
Knowledge of programming is perhaps the most important and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include the following:
Data sets are basically a collection of data. Algorithms are written to work on these data sets, therefore it is very essential to have a command over one or more programming languages.
It is heavily preferred in several data science projects for processing of large data sets. Tools are evolving for pulling information out of Hadoop clusters:
The average income of Data Scientist in Los Angeles is $98,294.
As compared to the average salary of a Data Scientist of $100,450 in New Jersey, the salary in Los Angeles is $2,156 less.
In Los Angeles, the average salary of a data scientist is $98,294 as compared to $110,925 in Chicago.
The average income of a data scientist in Los Angeles is $98,294 as compared to $125,310 in Boston.
In California, cities like San Francisco and San Diego have an average pay of $119,953 and $97,183 respectively for data scientists.
The Data Scientists in California are in high demand right now.
Here are the benefits of being a Data Scientist in Los Angeles:
Being a Data Scientist in Los Angeles offers several perks and advantages. There are opportunities to connect with different people in various conferences, summit, and meetups. Data Scientists play a major role in gathering useful insights after analyzing the raw data. This puts them in connection with top-level executives. Also, they have the luxury to work in the field that a person is interested in.
In Los Angeles, companies hiring Data Scientists include Snap Inc. and Capital Group.
|1.||Data Science Salon, Los Angeles||November 7, 2019||Red Bull Media House|
|2.||Data Con LA||August 17, 2019||The University of Southern California|
|3.||IDEAS SoCal AI & Data Science Conference 2019, Los Angeles||Sat, October 26, 2019||Los Angeles Convention Center|
|4.||SatRday LA – R Conference||April 6, 2019||UCLA James West Alumni Center|
|5.||Microsoft Reporting & Analytics by Ted Stathakis (#SQLSatLA)||Friday, June 14, 2019||Loyola Marymount University (LMU) in Playa Vista |
|6.||ACM IUI 2019, Los Angeles||March 16 to March 20, 2019||Marriott Marina Del Re|
|7.||IEEE BigData 2019: IEEE International Conference on Big Data, Los Angeles, CA, USA||Dec 9, 2019 - Dec 12, 2019||Los Angeles, CA, USA|
|8.||John Langford @ ZEFR||Saturday, June 15, 2019||ZEFR, 4101 Redwood Ave · Marina Del Rey, ca|
|9.||Using Apache Cassandra and Apache Kafka to Scale Next Gen Applications||Thursday, May 9, 2019||Verizon Digital Media Service|
|10.||ROpenSci Unconference, Los Angeles (USA)||May 25, 2019-26, 2019||Los Angeles (USA)|
1. Data Science Salon, Los Angeles
2. Data Con LA, Los Angeles
3. IDEAS SoCal AI & Data Science Conference 2019, Los Angeles
4. SatRday LA – R Conference, Los Angeles
5. Microsoft Reporting & Analytics by Ted Stathakis (#SQLSatLA), Los Angeles
6. ACM IUI 2019, Los Angeles
7. IEEE BigData 2019: IEEE International Conference on Big Data, Los Angeles
8. John Langford @ ZEFR, Los Angeles
9. Using Apache Cassandra and Apache Kafka to Scale Next Gen Applications, Los Angeles
10. ROpenSci Unconference, Los Angeles (USA)
|1.||XLIVE Data & Analytics Summit, executive-level forum across music, entertainment, sports, and culinary industries||4 April 2017, - 5 April, 2017|
|2.||Southern California Data Science Conference 2017||October 22, 2017||Visit Pasadena, 300 E Green St, Pasadena, CA 91101, USA|
|3.||Data Science SALON||14 December, 2017|
|4.||XLIVE Data & Analytics Summit||3 April, 2018 - 4 April, 2018||Hudson Loft, 1200, S Hope St, Los Angeles, CA 90015|
|5.||Big Data Day LA||11 August, 2018|
3670 Trousdale Pkwy, Los, Angeles, CA 90089, United States
|6.||Data Science Salon||13 September, 2018|
Red Bull Media House, 1740 Stewart St, Santa Monica, CA 90404
|7.||Converge: The Intersection of Data Science, Market Research & Analytics||4 - 5 December, 2018|
The Westin Bonaventure Hotel & Suites, 404, S. Figueroa Street, Los Angeles, CA 90071
1. XLIVE Data & Analytics Summit, executive-level forum across music, entertainment, sports, and culinary industries, Los Angeles
2. Southern California Data Science Conference 2017, Los Angeles
3. Data Science SALON, Los Angeles
4. XLIVE Data & Analytics Summit, Los Angeles
5. Big Data Day LA, Los Angeles
6. Data Science Salon, Los Angeles
7. Converge: The Intersection of Data Science, Market Research & Analytics, Los Angeles
Here is the logical sequence of steps you should follow to get a job as a Data Scientist.
Follow the below steps to increase your chances of success if you are an aspiring data scientist:
A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs.
In this modern business scenario that is generating tons of data every day, the role of a Data Scientist is becoming all the more important. This is because the data generated is a gold mine of patterns and ideas that could prove to be very helpful in the advancement of a business. It is up to the data scientist to extract the relevant information and make sense of it in order to benefit the business.
Data Scientist Roles & Responsibilities:
The average annual salary of a Data Scientist in Los Angeles, CA is $128,189. A Data analyst earns upto $62,088 per year while a database administrator makes about $73,622 per year.
The career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: A business intelligence analyst is responsible for analyzing data that is used by a business or organization that supports it in decision making. They perform tasks such as defining, reporting on or otherwise developing new structures for business intelligence to serve a specific purpose. Report writing can be a vital element for this role. They ensure that the business is always in the most favorable position by comparing data to competitors and observing industrial trends then creating reports and communicating the same to the organization.
Data Mining Engineer: Data Mining Engineer creates and boosts statistical and predictive models and algorithms to vast data sets. They will be working as an engineer to productize, facilitate and implement the systems needed for the analysis and must be able to explain and present hypotheses and analysis results to a wide audience in a clear and concise manner.
A Data Mining Engineer has underlined functions:
Data Architect: A data architect builds and maintains a company’s database by identifying structural and installation solution.
Data Scientist: The data scientist is an analytical data expert having the technical skills required to solve complex problems. Data scientists are responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy. The various roles include the following:
Chief Data Officer: The chief data officer is a senior executive who surveys a wide range of data related functions through data processing, data mining, analysis and other measures for entity-wide governance and utilization information as a resource.
Below are the top professional organizations for data scientists in Los Angeles, CA –
Referrals are the most effective way to get hired. Some of the other ways to network with data scientists in Los Angeles, CA are:
There are several career options for a data scientist –
We have compiled the key points, which the employers generally look for while hiring data scientists:
As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.
Follow these steps to successfully install Python 3 on windows:
Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:
python -m pip install -U pip
Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.
To install python 3 on Mac OS X, just follow the below steps:
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ brew install python
Overall, the training session at KnowledgeHut was a great experience. Learnt many things, it is the best training institution which I believe. My trainer covered all the topics with live examples. Really, the training session was worth spending.
I am glad to have attended KnowledgeHut’s training program. Really I should thank my friend for referring me here. I was impressed with the trainer, explained advanced concepts deeply with better examples. Everything was well organized. I would like to refer some of their courses to my peers as well.
Knowledgehut is the best training institution which I believe. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask feedback every time and clear all the doubts.
KnowledgeHut is a great platform for beginners as well as the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas and the concepts.
My special thanks to the trainer for his dedication, learned many things from him. I would also thank for the support team for their patience. It is well-organised, great work Knowledgehut team!
The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.
The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions. Thanks to KnowledgeHut, looking forward to more such workshops.
The course materials were designed very well with all the instructions. The training session gave me a lot of exposure and various opportunities and helped me in growing my career.
Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.
Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.
By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.
Tools and Technologies used for this course are
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Python and data science experts who have years of industry experience.
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor