Is only Microsoft Excel will give a good career in data analyst?
Although MS Excel is one of the key skills for a data analayst or a data scientist, you cannot get a job just by having a great hold on excel. There are lot more skill sets that you should be knowing. Apart from technical skills, domain knowledge is also one of the most crucial part of a data analyst career.
No, it’s just a part of the whole process. You need to have knowledge on various other tools too. I’m mentioning some of the most vital below with reason too.
In python with Pandas, you can just do anything! You can perform advanced data manipulations and numeric analysis using data frames. Pandas support multiple file-formats; for example, you can import data from Excel spreadsheets to processing sets for time-series analysis. (By definition - Time-series analysis is a statistical technique that analyses time series data, i.e., data collected at a certain interval of time). Pandas is a powerful tool for data visualizing, data masking, merging, indexing and grouping data, data cleaning, and many more.
Other libraries, such as Scipy, Scikit-learn, StatsModels, are used for statistical modeling, mathematical algorithms, machine learning, and data mining. Matplotlib, seaborn, and vispy are packages for data visualization and graphical analysis.
Top Companies that use Python for data analysis are Spotify, Netflix, NASA, Google and CERN and many more.
R has a steep learning curve and needs some amount of working knowledge of coding. However, it is a great language when it comes to syntax and consistency. R is a winner when it comes to EDA(By definition - In statistics, exploratory data analysis(EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods). Data manipulation in R is easy with packages such as plyr, dplyr, and tidy. R is excellent when it comes to data visualization and analysis with packages such as ggplot, lattice, ggvis, etc.
R is used by Facebook, Google, Twitter and Uber.
SAS’s Visual Analytics software is a powerful tool for interactive dashboards, reports, BI, self-service analytics, Text analytics, and smart visualizations. SAS is widely used in the pharmaceutical industry, BI, and weather forecasting. Since SAS is a paid-for service, it has a 24X7 customer support to help with your doubts. Google, Facebook, Netflix, Twitter are a few companies that use SAS.
SAS is used for clinical research reporting in Novartis and Covance, Citibank, Apple, Deloitte and much more use SAS for predictive analysis
Tableau provides fast analytics, it can explore any type of data – spreadsheets, databases, data on Hadoop and cloud services. It is easy to use as it has a powerful drag and drop features that anyone with an intuitive mind can handle. The data visualization with smart dashboards can be shared within seconds.
Top companies that use Tableau are Amazon, Citibank, Barclays, LinkedIn, and many more.
- Apache Spark
Spark Is an integrated analytics engine for Big Data processing designed for developers, researchers, and data scientists. It is free, open-source and a wide range of developers contribute to its development. It is a high-performance tool and works well for batch and streaming data. Learning Spark is easy, and you can use it interactively from the Scala, Python, R, and SQL shells too. Spark can run on any platform such as Hadoop, Apache Mesos, standalone, or in the cloud. It can access diverse data sources.
Uber, Slack, Shopify, and many other companies use Apache Spark for data analytics.
No, Data analytics is much more than ms excel.
You need to learn statistics, machine learning, model implementation, interpretation of results. you need to understand the descriptive, inquisitive, prescriptive and predictive part . You need to visualize data, generate insights and solutions it. Transforming business problem to data science problem and then implementing a solution to it . So yeah, there are lot many things apart from excel that you need to learn.